The use of the MPI communication library in the NAS Parallel Benchmark

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mpi应用实例 -回复

mpi应用实例-回复MPIApplication ExamplesIntroduction:Message Passing Interface (MPI) is a widely used communication protocol and programming model for parallel computing. It allows multiple processes to work together to solve a complex problem by sending and receiving messages. In this article, we will explore some real-world applications of MPI and discuss how this technology is used to address various computational challenges.1. Weather and Climate Modeling:One crucial application of MPI is weather and climate modeling. Weather prediction models require a vast amount of data and processing power to simulate atmospheric conditions accurately. MPI enables scientists to split the computational workload across multiple nodes or processors and exchange data as needed. Each processor works on a designated section of the simulation, and they communicate with each other by sending and receiving messages containing essential information, such as temperature,pressure, and wind speed. By using MPI, weather models can run efficiently on high-performance computing clusters, providing more accurate predictions for meteorologists and disaster management agencies.2. Computational Fluid Dynamics (CFD):CFD is a computational tool used to simulate fluid flow and heat transfer phenomena in various industries, such as aerospace, automotive, and energy. MPI plays a vital role in solving CFD problems by allowing parallel processing of large grid systems. Grid cells or elements are divided among different processors, and each processor calculates the flow properties for its allocated grid cells. MPI facilitates the exchange of information between neighboring processors to update boundary conditions and ensure a consistent solution across the entire domain. By utilizing MPI's collective communication operations, such as scatter, gather, and reduce, CFD simulations can be performed faster, enabling engineers and designers to optimize their designs efficiently.3. Molecular Dynamics Simulations:Molecular dynamics simulations are widely used in chemistry, biochemistry, and material science to study the structure and behavior of atoms and molecules. In these simulations, MPI is used to distribute the calculation tasks among different processors, each responsible for a subset of atoms in the system. The processors communicate to exchange forces and update particle positions to simulate the movement of atoms over time accurately. By employing advanced MPI features, such as non-blocking communication and parallel I/O, scientists can effectively simulate large systems and investigate chemical reactions, protein folding, and material properties.4. Data Analytics and Machine Learning:As the volume of data continues to grow exponentially, the need for efficient data analysis and machine learning algorithms has increased. MPI can be applied to distributed data analytics applications, where multiple processors work on different subsets of data simultaneously. Each processor performs calculations and uses MPI to communicate partial results or exchange intermediate data when needed. By utilizing MPI's collective operations like broadcast, reduce, and all-to-all communication, distributedmachine learning algorithms can efficiently train models on massive datasets. MPI provides the capability to scale up computations to large clusters, enabling researchers to extract meaningful insights from enormous amounts of data.Conclusion:From weather modeling and computational fluid dynamics to molecular dynamics simulations and data analytics, MPI finds extensive application in a wide range of scientific and engineering domains. As parallel computing becomes a necessity to solve complex problems, the efficient communication provided by MPI proves invaluable. With the advancements in high-performance computing and the continual development of MPI libraries, researchers and developers can harness the power of parallelism to tackle ever more significant computational challenges and propel scientific discoveries forward.。

lam_MPI

Ohio Supercomputer Center The Ohio State UniversityUPERCOMPUTER S OHIOC E N T E R LAM is a parallel processing environment and development system for a network of independent computers. It features the Message-Passing Interface (MPI)programming standard,supported by extensive monitoring and debugging M / MPI Key Features:•full implementation of the MPI standard •extensive monitoring and debugging tools,runtime and post-mortem •heterogeneous computer networks •add and delete nodes •node fault detection and recovery •MPI extensions and LAM programming supplements •direct communication between application processes •robust MPI resource management •MPI-2 dynamic processes •multi-protocol communication (shared memory and network)MPI Primer /Developing With LAM2This document is organized into four major chapters. It begins with a tuto-rial covering the simpler techniques of programming and operation. New users should start with the tutorial. The second chapter is an MPI program-ming primer emphasizing the commonly used routines. Non-standard extensions to MPI and additional programming capabilities unique to LAM are separated into a third chapter. The last chapter is an operational refer-ence.It describes how to conﬁgure and start a LAM multicomputer,and how to monitor processes and messages.This document is user oriented. It does not give much insight into how the system is implemented. It does not detail every option and capability of every command and routine.An extensive set of manual pages cover all the commands and internal routines in great detail and are meant to supplement this document.The reader will note a heavy bias towards the C programming language,especially in the code samples.There is no Fortran version of this document.The text attempts to be language insensitive and the appendices contain For-tran code samples and routine prototypes.We have kept the font and syntax conventions to a minimum.code This font is used for things you type on the keyboard orsee printed on the screen.We use it in code sections andtables but not in the main text.<symbol>This is a symbol used to abstract something you wouldtype. We use this convention in commands.Section Italics are used to cross reference another section in thedocument or another document. Italics are also used todistinguish LAM commands.How to UseThisDocument3How to Use This Document 2LAM Architecture 7Debugging 7MPI Implementation 8How to Get LAM 8LAM / MPI Tutorial IntroductionProgramming Tutorial 9The World of MPI 10Enter and Exit MPI 10Who Am I; Who Are They? 10Sending Messages 11Receiving Messages 11Master / Slave Example 12Operation Tutorial 15Compilation 15Starting LAM 15Executing Programs 16Monitoring 17Terminating the Session 18MPI Programming PrimerBasic Concepts 19Initialization 21Basic Parallel Information 21Blocking Point-to-Point 22Send Modes 22Standard Send 22Receive 23Status Object 23Message Lengths 23Probe 24Nonblocking Point-to-Point 25Request Completion 26Probe 26Table ofContents4Message Datatypes 27Derived Datatypes 28Strided Vector Datatype 28Structure Datatype 29Packed Datatype 31Collective Message-Passing 34Broadcast 34Scatter 34Gather 35Reduce 35Creating Communicators 38Inter-communicators 40Fault Tolerance 40Process Topologies 41Process Creation 44Portable Resource Specification 45 Miscellaneous MPI Features 46Error Handling 46Attribute Caching 47Timing 48LAM / MPI ExtensionsRemote File Access 50Portability and Standard I/O 51 Collective I/O 52Cubix Example 54Signal Handling 55Signal Delivery 55Debugging and Tracing 56LAM Command ReferenceGetting Started 57Setting Up the UNIX Environment 575 Node Mnemonics 57Process Identification 58On-line Help 58Compiling MPI Programs 60Starting LAM 61recon 61lamboot 61Fault Tolerance 61tping 62wipe 62Executing MPI Programs 63mpirun 63Application Schema 63Locating Executable Files 64Direct Communication 64Guaranteed Envelope Resources 64Trace Collection 65lamclean 65Process Monitoring and Control 66mpitask 66GPS Identification 68Communicator Monitoring 69Datatype Monitoring 69doom 70Message Monitoring and Control 71mpimsg 71Message Contents 72bfctl 72Collecting Trace Data 73lamtrace 73Adding and Deleting LAM Nodes 74lamgrow 74lamshrink 74File Monitoring and Control 75fstate 75fctl 756Writing a LAM Boot Schema 76Host File Syntax 76Low Level LAM Start-up 77Process Schema 77hboot 77Appendix A: Fortran Bindings 79 Appendix B: Fortran Example Program 857LAM runs on each computer as a single daemon (server) uniquely struc-tured as a nano-kernel and hand-threaded virtual processes.The nano-kernel component provides a simple message-passing,rendez-vous service to local processes. Some of the in-daemon processes form a network communica-tion subsystem,which transfers messages to and from other LAM daemons on other machines.The network subsystem adds features such as packetiza-tion and buffering to the base synchronization. Other in-daemon processes are servers for remote capabilities, such as program execution and parallel ﬁle access.The layering is quite distinct:the nano-kernel has no connection with the network subsystem, which has no connection with the ers can conﬁgure in or out services as necessary.The unique software engineering of LAM is transparent to users and system administrators, who only see a conventional daemon. System developers can de-cluster the daemon into a daemon containing only the nano-kernel and several full client processes. This developers’ mode is still transparent to users but exposes LAM’s highly modular components to simpliﬁed indi-vidual debugging.It also reveals LAM’s evolution from Trollius,which ran natively on scalable multicomputers and joined them to a host network through a uniform programming interface.The network layer in LAM is a documented,primitive and abstract layer on which to implement a more powerful communication standard like MPI (PVM has also been implemented).A most important feature of LAM is hands-on control of the multicomputer.There is very little that cannot be seen or changed at runtime. Programs residing anywhere can be executed anywhere,stopped,resumed,killed,and watched the whole time. Messages can be viewed anywhere on the multi-computer and buffer constraints tuned as experience with the application LAMArchitecturelocal msgs, client mgmt network msgs MPI, client / server cmds, apps, GUIs Figure 1: LAM’s Layered Design Debugging8dictates.If the synchronization of a process and a message can be easily dis-played, mismatches resulting in bugs can easily be found. These and other services are available both as a programming library and as utility programs run from any shell.MPI synchronization boils down to four variables:context,tag,source rank,and destination rank.These are mapped to LAM’s abstract synchronization at the network layer. MPI debugging tools interpret the LAM information with the knowledge of the LAM / MPI mapping and present detailed infor-mation to MPI programmers.A signiﬁcant portion of the MPI speciﬁcation can be and is implemented within the runtime system and independent of the underlying environment.As with all MPI implementations, LAM must synchronize the launch of MPI applications so that all processes locate each other before user code is entered. The mpirun command achieves this after ﬁnding and loading the program(s) which constitute the application. A simple SPMD application can be speciﬁed on the mpirun command line while a more complex conﬁg-uration is described in a separate ﬁle, called an application schema.MPI programs developed on LAM can be moved without source code changes to any other platform that supports M installs anywhere and uses the shell’s search path at all times to ﬁnd LAM and application executables.A multicomputer is speciﬁed as a simple list of machine names in a ﬁle, which LAM uses to verify access, start the environment, and remove M is freely available under a GNU license via anonymous ftp from.MPIImplementationHow to Get LAM9LAM / MPI Tutorial Introduction The example programs in this section illustrate common operations in MPI.You will also see how to run and debug a program with LAM.For basic applications, MPI is as easy to use as any other message-passing library.The ﬁrst program is designed to run with exactly two processes.Oneprocess sends a message to the other and then both terminate.Enter the fol-lowing code in trivial.c or obtain the source from the LAM source distribu-tion (examples/trivial/trivial.c)./** Transmit a message in a two process system.*/#include <mpi.h>#define BUFSIZE 64int buf[64];intmain(argc, argv)int argc;char *argv[];{int size, rank;MPI_Status status;/** Initialize MPI.*/MPI_Init(&argc, &argv);/** Error check the number of processes.* Determine my rank in the world group.ProgrammingTutorial10 * The sender will be rank 0 and the receiver, rank 1. */MPI_Comm_size(MPI_COMM_WORLD, &size);if (2 != size) {MPI_Finalize();return(1);}MPI_Comm_rank(MPI_COMM_WORLD, &rank);/* * As rank 0, send a message to rank 1. */if (0 == rank) {MPI_Send(buf, sizeof(buf), MPI_INT, 1, 11,MPI_COMM_WORLD);}/* * As rank 1, receive a message from rank 0. */else {MPI_Recv(buf, sizeof(buf), MPI_INT, 0, 11,MPI_COMM_WORLD, &status);}MPI_Finalize();return(0);}Note that the program uses standard C program structure, statements, vari-able declarations and types, and functions.Processes are represented by a unique “rank” (integer) and ranks are num-bered 0, 1, 2, ..., N-1. MPI_COMM_WORLD means “all the processes in the MPI application.” It is called a communicator and it provides all infor-mation necessary to do message-passing. Portable libraries do more with communicators to provide synchronization protection that most other mes-sage-passing systems cannot handle.As with other systems, two routines are provided to initialize and cleanup an MPI process:MPI_Init(int *argc, char ***argv);MPI_Finalize(void);Typically, a process in a parallel application needs to know who it is (its rank)and how many other processes exist.A process ﬁnds out its own rankby calling MPI_Comm_rank().The World ofMPIEnter and ExitMPIWho Am I; WhoAre They?MPI_Comm_rank(MPI_Comm comm, int *rank);The total number of processes is returned by MPI_Comm_size().MPI_Comm_size(MPI_Comm comm, int *size);A message is an array of elements of a given datatype.MPI supports all the basic datatypes and allows a more elaborate application to construct new datatypes at runtime.A message is sent to a speciﬁc process and is marked by a tag (integer)spec-iﬁed by the user. Tags are used to distinguish between different message types a process might send/receive.In the example program above,the addi-tional synchronization offered by the tag is unnecessary.Therefore,any ran-dom value is used that matches on both sides.MPI_Send(void *buf, int count, MPI_Datatype dtype, int dest, int tag, MPI_Comm comm);A receiving process speciﬁes the tag and the rank of the sending process.MPI_ANY_TAG and MPI_ANY_SOURCE may be used to receive a mes-sage of any tag and from any sending process.MPI_Recv(void *buf, int count, MPI_Datatypedtype, int source, int tag, MPI_Comm comm,MPI_Status *status);Information about the received message is returned in a status variable. If wildcards are used, the received message tag is status.MPI_TAG and the rank of the sending process is status.MPI_SOURCE.Another routine, not used in the example program, returns the number of datatype elements received.It is used when the number of elements received might be smaller than number speciﬁed to MPI_Recv().It is an error to send more elements than the receiving process will accept.MPI_Get_count(MPI_Status, &status,MPI_Datatype dtype, int *nelements);SendingMessagesReceivingMessagesThe following example program is a communication skeleton for a dynam-ically load balanced master/slave application. The source can be obtainedfrom the LAM source distribution (examples/trivial/ezstart.c).The program is designed to work with a minimum of two processes:one master and one slave.#include <mpi.h>#define WORKTAG 1#define DIETAG 2#define NUM_WORK_REQS 200static void master();static void slave();/**main* This program is really MIMD, but is written SPMD for * simplicity in launching the application.*/intmain(argc, argv)int argc;char *argv[];{int myrank;MPI_Init(&argc, &argv);MPI_Comm_rank(MPI_COMM_WORLD,/* group of everybody */&myrank);/* 0 thru N-1 */if (myrank == 0) {master();} else {slave();}MPI_Finalize();return(0);}/**master* The master process sends work requests to the slaves * and collects results.*/static voidmaster(){int ntasks, rank, work;double result;MPI_Status status;MPI_Comm_size(MPI_COMM_WORLD,&ntasks);/* #processes in app */Master / SlaveExample/** Seed the slaves.*/work = NUM_WORK_REQS;/* simulated work */for (rank = 1; rank < ntasks; ++rank) {MPI_Send(&work,/* message buffer */1,/* one data item */MPI_INT,/* of this type */rank,/* to this rank */WORKTAG,/* a work message */MPI_COMM_WORLD);/* always use this */ work--;}/** Receive a result from any slave and dispatch a new work* request until work requests have been exhausted.*/while (work > 0) {MPI_Recv(&result,/* message buffer */1,/* one data item */MPI_DOUBLE,/* of this type */MPI_ANY_SOURCE,/* from anybody */MPI_ANY_TAG,/* any message */MPI_COMM_WORLD,/* communicator */&status);/* recv’d msg info */MPI_Send(&work, 1, MPI_INT, status.MPI_SOURCE,WORKTAG, MPI_COMM_WORLD);work--;/* simulated work */ }/** Receive results for outstanding work requests.*/for (rank = 1; rank < ntasks; ++rank) {MPI_Recv(&result, 1, MPI_DOUBLE, MPI_ANY_SOURCE,MPI_ANY_TAG, MPI_COMM_WORLD, &status);}/** Tell all the slaves to exit.*/for (rank = 1; rank < ntasks; ++rank) {MPI_Send(0, 0, MPI_INT, rank, DIETAG,MPI_COMM_WORLD);}}/**slave* Each slave process accepts work requests and returns* results until a special termination request is received. */static voidslave(){double result;int work;MPI_Status status;for (;;) {MPI_Recv(&work, 1, MPI_INT, 0, MPI_ANY_TAG,MPI_COMM_WORLD, &status);/** Check the tag of the received message.*/if (status.MPI_TAG == DIETAG) {return;}sleep(2);result = 6.0;/* simulated result */MPI_Send(&result, 1, MPI_DOUBLE, 0, 0,MPI_COMM_WORLD);}}The workings of ranks,tags and message lengths should be mastered before constructing serious MPI applications.Before running LAM you must establish certain environment variables and search paths for your shell. Add the following commands or equivalent to your shell start-up ﬁle (.cshrc,assuming C shell).Do not add these to your .login as they would not be effective on remote machines when rsh is used to start LAM.setenv LAMHOME <LAM installation directory>set path = ($path $LAMHOME/bin)The local system administrator,or the person who installed LAM,will know the location of the LAM installation directory. After editing the shell start-up ﬁle,invoke it to establish the new values.This is not necessary on subse-quent logins to the UNIX system.% source .cshrc Many LAM commands require one or more nodeids.Nodeids are speciﬁed on the command line as n<list>, where <list> is a list of comma separated nodeids or nodeid ranges.n1n1,3,5-10The mnemonic ‘h’refers to the local node where the command is typed (as in ‘here’).Any native C compiler is used to translate LAM programs for execution.All LAM runtime routines are found in a few libraries. LAM provides a wrap-ping command called hcc which invokes cc with the proper header and library directories, and is used exactly like the native cc.% hcc -o trivial trivial.c -lmpi The major,internal LAM libraries are automatically linked.The MPI library is explicitly linked.Since LAM supports heterogeneous computing,it is up to the user to compile the source code for each of the various CPUs on their respective machines. After correcting any errors reported by the compiler,proceed to starting the LAM session.Before starting LAM,the user speciﬁes the machines that will form the mul-ticomputer. Create a host ﬁle listing the machine names, one on each line.An example ﬁle is given below for the machines “ohio” and “osc”. Lines starting with the # character are treated as comment lines.OperationTutorialCompilationStarting LAM# a 2-node LAM ohio osc The ﬁrst machine in the host ﬁle will be assigned nodeid 0, the second nodeid 1,etc.Now verify that the multicomputer is ready to run LAM.The recon tool checks if the user has access privileges on each machine in the multicomputer and if LAM is installed and accessible.% recon -v <host file>If recon does not report a problem, proceed to start the LAM session with the lamboot tool.% lamboot -v <host file>The -v (verbose)option causes lamboot to report on the start-up process as it progresses. You should return to the your own shell’s prompt. LAM pre-sents no special shell or interface environment.Even if all seems well after start-up,verify communication with each node.tping is a simple conﬁdence building command for this purpose.% tping n0Repeat this command for all nodes or ping all the nodes at once with the broadcast mnemonic,N.tping responds by sending a message between the local node (where the user invoked tping)and the speciﬁed node.Successful execution of tping proves that the target node, nodes along the route from the local node to the target node,and the communication links between them are working properly. If tping fails, press Control-Z, terminate the session with the wipe tool and then restart the system.See Terminating the Session .To execute a program,use the mpirun command.The ﬁrst example program is designed to run with two processes.The -c <#>option runs copies of thegiven program on nodes selected in a round-robin manner.% mpirun -v -c 2 trivialThe example invocation above assumes that the program is locatable on the machine on which it will run. mpirun can also transfer the program to the target node before running it.Assuming your multicomputer for this tutorial is homogeneous, you can use the -s h option to run both processes.% mpirun -v -c 2 -s h trivialExecutingProgramsIf the processes executed correctly,they will terminate and leave no traces.If you want more feedback,try using tprintf()functions within the program.The ﬁrst example program runs too quickly to be monitored.Try changingthe tag in the call to MPI_Recv() to 12 (from 11). Recompile the program and rerun it as before. Now the receiving process cannot synchronize with the message from the send process because the tags are unequal.Look at the status of all MPI processes with the mpitask command.You will notice that the receiving process is blocked in a call to MPI_Recv()- a synchronizing message has not been received. From the code we know this is process rank 1in the MPI application,which is conﬁrmed in the ﬁrst column,the MPI task identiﬁcation.The ﬁrst number is the rank within the world group.The second number is the rank within the communicator being used by MPI_Recv(), in this case (and in many applications with simple communication structure)also the world group.The speciﬁed source of the message is likewise identiﬁed.The synchronization tag is 12and the length of the receive buffer is 64 elements of type MPI_INT.The message was transferred from the sending process to a system buffer en route to process rank 1.MPI_Send()was able to return and the process has called MPI_Finalize().System buffers,which can be thought of as message queues for each MPI process,can be examined with the mpimsg command.The message shows that it originated from process rank 0 usingMPI_COMM_WORLD and that it is waiting in the message queue of pro-cess rank 1, the destination. The tag is 11 and the message contains 64 ele-ments of type MPI_INT. This information corresponds to the arguments given to MPI_Send(). Since the application is faulty and will never com-plete, we will kill it with the lamclean command.% lamclean -vMonitoring % mpitaskTASK (G/L)FUNCTION PEER|ROOT TAG COMM COUNT DATATYPE 0/0 trivialFinalize 1/1 trivial Recv 0/012WORLD 64INT % mpimsgSRC (G/L)DEST (G/L)TAG COMM COUNT DATATYPE MSG 0/01/111WORLD 64INT n1,#0The LAM session should be in the same state as after invoking lamboot.You can also terminate the session and restart it with lamboot,but this is a much slower operation. You can now correct the program, recompile and rerun.To terminate LAM, use the wipe tool. The host ﬁle argument must be the same as the one given to lamboot.% wipe -v <host file>Terminating theSessionMPI Programming PrimerBasic ConceptsThrough Message Passing Interface(MPI)an application views its parallelenvironment as a static group of processes.An MPI process is born into theworld with zero or more siblings. This initial collection of processes iscalled the world group.A unique number,called a rank,is assigned to eachmember process from the sequence0through N-1,where N is the total num-ber of processes in the world group.A member can query its own rank andthe size of the world group.Processes may all be running the same program(SPMD) or different programs (MIMD). The world group processes maysubdivide,creating additional subgroups with a potentially different rank ineach group.A process sends a message to a destination rank in the desired group.A pro-cess may or may not specify a source rank when receiving a message.Mes-sages are further ﬁltered by an arbitrary, user speciﬁed, synchronizationinteger called a tag, which the receiver may also ignore.An important feature of MPI is the ability to guarantee independent softwaredevelopers that their choice of tag in a particular library will not conﬂictwith the choice of tag by some other independent developer or by the enduser of the library.A further synchronization integer called a context is allo-cated by MPI and is automatically attached to every message.Thus,the fourmain synchronization variables in MPI are the source and destination ranks,the tag and the context.A communicator is an opaque MPI data structure that contains informationon one group and that contains one context.A communicator is an argumentto all MPI communication routines.After a process is created and initializes MPI, three predeﬁned communicators are available.MPI_COMM_WORLD the world groupMPI_COMM_SELF group with one member, myselfMPI_COMM_PARENT an intercommunicator between two groups:my world group and my parent group (SeeDynamic Processes.)Many applications require no other communicators beyond the world com-municator.If new subgroups or new contexts are needed,additional commu-nicators must be created.MPI constants, templates and prototypes are in the MPI header ﬁle, mpi.h. #include <mpi.h>MPI_Init Initialize MPI state.MPI_Finalize Clean up MPI state.MPI_Abort Abnormally terminate.MPI_Comm_size Get group process count.MPI_Comm_rank Get my rank within process group.MPI_Initialized Has MPI been initialized?The ﬁrst MPI routine called by a program must be MPI_Init(). The com-mand line arguments are passed to MPI_Init().MPI_Init(int *argc, char **argv[]);A process ceases MPI operations with MPI_Finalize().MPI_Finalize(void);In response to an error condition,a process can terminate itself and all mem-bers of a communicator with MPI_Abort().The implementation may report the error code argument to the user in a manner consistent with the underly-ing operation system.MPI_Abort (MPI_Comm comm, int errcode);Two numbers that are very useful to most parallel applications are the total number of parallel processes and self process identiﬁcation. This informa-tion is learned from the MPI_COMM_WORLD communicator using the routines MPI_Comm_size() and MPI_Comm_rank().MPI_Comm_size (MPI_Comm comm, int *size);MPI_Comm_rank (MPI_Comm comm, int *rank);Of course, any communicator may be used, but the world information is usually key to decomposing data across the entire parallel application.InitializationBasic ParallelInformationMPI_Send Send a message in standard mode.MPI_Recv Receive a message.MPI_Get_count Count the elements received.MPI_Probe Wait for message arrival.MPI_Bsend Send a message in buffered mode.MPI_Ssend Send a message in synchronous mode.MPI_Rsend Send a message in ready mode.MPI_Buffer_attach Attach a buffer for buffered sends.MPI_Buffer_detach Detach the current buffer.MPI_Sendrecv Send in standard mode, then receive.MPI_Sendrecv_replace Send and receive from/to one area.MPI_Get_elements Count the basic elements received.This section focuses on blocking,point-to-point,message-passing routines.The term “blocking”in MPI means that the routine does not return until the associated data buffer may be reused. A point-to-point message is sent by one process and received by one process.The issues of ﬂow control and buffering present different choices in design-ing message-passing primitives. MPI does not impose a single choice but instead offers four transmission modes that cover the synchronization,data transfer and performance needs of most applications.The mode is selected by the sender through four different send routines, all with identical argu-ment lists. There is only one receive routine. The four send modes are:standard The send completes when the system can buffer the mes-sage (it is not obligated to do so)or when the message is received.buffered The send completes when the message is buffered in application supplied space, or when the message is received.synchronous The send completes when the message is received.ready The send must not be started unless a matching receive has been started. The send completes immediately.Standard mode serves the needs of most applications.A standard mode mes-sage is sent with MPI_Send().MPI_Send (void *buf, int count, MPI_Datatypedtype, int dest, int tag, MPI_Comm comm); BlockingPoint-to-PointSend ModesStandard SendAn MPI message is not merely a raw byte array. It is a count of typed ele-ments.The element type may be a simple raw byte or a complex data struc-ture. See Message Datatypes .The four MPI synchronization variables are indicated by the MPI_Send()parameters. The source rank is the caller’s. The destination rank and mes-sage tag are explicitly given.The context is a property of the communicator.As a blocking routine, the buffer can be overwritten when MPI_Send()returns.Although most systems will buffer some number of messages,espe-cially short messages,without any receiver,a programmer cannot rely upon MPI_Send() to buffer even one message. Expect that the routine will not return until there is a matching receiver.A message in any mode is received with MPI_Recv().MPI_Recv (void *buf, int count, MPI_Datatype dtype, int source, int tag, MPI_Comm comm,MPI_Status *status);Again the four synchronization variables are indicated,with source and des-tination swapping places. The source rank and the tag can be ignored with the special values MPI_ANY_SOURCE and MPI_ANY_TAG.If both these wildcards are used, the next message for the given communicator is received.An argument not present in MPI_Send()is the status object pointer.The sta-tus object is ﬁlled with useful information when MPI_Recv()returns.If the source and/or tag wildcards were used,the actual received source rank and/or message tag are accessible directly from the status object.status.MPI_SOURCE the sender’s rank status.MPI_TAG the tag given by the sender It is erroneous for an MPI program to receive a message longer than thespeciﬁed receive buffer. The message might be truncated or an error condi-tion might be raised or both.It is completely acceptable to receive a message shorter than the speciﬁed receive buffer. If a short message may arrive, the application can query the actual length of the message withMPI_Get_count().MPI_Get_count (MPI_Status *status,MPI_Datatype dtype, int *count);ReceiveStatus ObjectMessage Lengths。

NVIDIA Collective Communication Library (NCCL) 安装指

DU-08730-210_v01 | May 2018 Installation GuideTABLE OF CONTENTS Chapter 1. Overview (1)Chapter 2. Prerequisites (3)2.1. Software Requirements (3)2.2. Hardware Requirements (3)Chapter 3. Installing NCCL (4)3.1. Ubuntu 14.04 LTS And Ubuntu 16.04 LTS (4)3.2. Other Distributions (5)Chapter 4. Using NCCL (6)Chapter 5. Migrating From NCCL 1 T o NCCL 2 (7)Chapter 6. Troubleshooting (9)6.1. Support (9)The NVIDIA® Collective Communications Library ™ (NCCL) (pronounced “Nickel”) is a library of multi-GPU collective communication primitives that are topology-aware and can be easily integrated into applications.Collective communication algorithms employ many processors working in concert to aggregate data. NCCL is not a full-blown parallel programming framework; rather, itis a library focused on accelerating collective communication primitives. The following collective operations are currently supported:‣AllReduce‣Broadcast‣Reduce‣AllGather‣ReduceScatterTight synchronization between communicating processors is a key aspect of collective communication. CUDA® based collectives would traditionally be realized through a combination of CUDA memory copy operations and CUDA kernels for local reductions. NCCL, on the other hand, implements each collective in a single kernel handling both communication and computation operations. This allows for fast synchronization and minimizes the resources needed to reach peak bandwidth.NCCL conveniently removes the need for developers to optimize their applications for specific machines. NCCL provides fast collectives over multiple GPUs both within and across nodes. It supports a variety of interconnect technologies including PCIe, NVLink™, InfiniBand Verbs, and IP sockets. NCCL also automatically patterns its communication strategy to match the system’s underlying GPU interconnect topology.Next to performance, ease of programming was the primary consideration in the design of NCCL. NCCL uses a simple C API, which can be easily accessed from a variety of programming languages. NCCL closely follows the popular collectives API defined by MPI (Message Passing Interface). Anyone familiar with MPI will thus find NCCL API very natural to use. In a minor departure from MPI, NCCL collectives take a “stream”argument which provides direct integration with the CUDA programming model. Finally, NCCL is compatible with virtually any multi-GPU parallelization model, for example:Overview‣single-threaded‣multi-threaded, for example, using one thread per GPU‣multi-process, for example, MPI combined with multi-threaded operation on GPUs NCCL has found great application in deep learning frameworks, where the AllReduce collective is heavily used for neural network training. Efficient scaling of neural network training is possible with the multi-GPU and multi node communication provided by NCCL.2.1. Software RequirementsEnsure your environment meets the following software requirements:‣glibc 2.19 or higher‣CUDA 8.0 or higher2.2. Hardware RequirementsNCCL supports all CUDA devices with a compute capability of 3.0 and higher. For the compute capability of all NVIDIA GPUs, check: CUDA GPUs.In order to download NCCL, ensure you are registered for the NVIDIA Developer Program.1.Go to: NVIDIA NCCL home page.2.Click Download.plete the short survey and click Submit.4.Accept the Terms and Conditions. A list of available download versions of NCCLdisplays.5.Select the NCCL version you want to install. A list of available resources displays.Refer to the following sections to choose the correct package depending on theLinux distribution you are using.3.1. Ubuntu 14.04 LTS And Ubuntu 16.04 LTSInstalling NCCL on Ubuntu requires you to first add a repository to the APT system containing the NCCL packages, then installing the NCCL packages through APT. There are two repositories available; a local repository and a network repository. Choosing the later is recommended to easily retrieve upgrades when newer versions are posted.1.Install the repository.‣For the local NCCL repository:sudo dpkg -i nccl-repo-<version>.deb‣For the network repository:sudo dpkg -i nvidia-machine-learning-repo-<version>.deb2.Update the APT database:sudo apt updateInstalling NCCL3.Install the libnccl2 package with APT. Additionally, if you need to compileapplications with NCCL, you can install the libnccl-dev package as well: If you are using the network repository, the following command will upgradeCUDA to the latest version.sudo apt install libnccl2 libnccl-devIf you prefer to keep an older version of CUDA, specify a specific version, forexample:sudo apt-get install libnccl2=2.0.0-1+cuda8.0 libnccl-dev=2.0.0-1+cuda8.0 Refer to the download page for exact package versions.3.2. Other DistributionsDownload the tar file package. For more information see Installing NCCL.1.Extract the NCCL package to your home directory or in /usr/local if installed asroot for all users:# cd /usr/local# tar xvf nccl-<version>.txz2.When compiling applications, specify the directory path to where you installedNCCL, for example /usr/local/nccl-<version>/.Using NCCL is similar to using any other library in your code. For example:1.Install the NCCL library onto your system.For more information, see Downloading NCCL.2.Modify your application to link to that library.3.Include the header file nccl.h in your application.4.Create a communicator.For more information, see Creating a Communicator in the NCCL Developer Guide.5.Familiarize yourself with the NCCL API documentation to maximize your usageperformance.If you are using NCCL 1.x and want to move to NCCL 2.x, be aware that the APIs have changed slightly. NCCL 2.x supports all of the collectives that NCCL 1.x supports, but with slight modifications to the API.In addition, NCCL 2.x also requires the usage of the Group API when a single thread manages NCCL calls for multiple GPUs.The following list summarizes the changes that may be required in usage of NCCL API when using an application has a single thread that manages NCCL calls for multiple GPUs, and is ported from NCCL 1.x to 2.x:InitializationIn 1.x, NCCL had to be initialized using ncclCommInitAll at a single thread or having one thread per GPU concurrently call ncclCommInitRank. NCCL 2.x retains these two modes of initialization. It adds a new mode with the Group API where ncclCommInitRank can be called in a loop, like a communication call, as shown below. The loop has to be guarded by the Group start and stop API. ncclGroupStart();for (int i=0; i<ngpus; i++) {cudaSetDevice(i);ncclCommInitRank(comms+i, ngpus, id, i);}ncclGroupEnd();CommunicationIn NCCL 2.x, the collective operation can be initiated for different devices by making calls in a loop, on a single thread. This is similar to the usage in NCCL 1.x. However, this loop has to be guarded by the Group API in 2.x. Unlike in 1.x, the application does not have to select the relevant CUDA device before making the communication API call. NCCL runtime internally selects the device associated with the NCCL communicator handle. For example:ncclGroupStart();for (int i=0; i<nLocalDevs; i++) {Migrating From NCCL 1 T o NCCL 2ncclAllReduce(..., comm[i], stream[i];}ncclGroupEnd();When using only one device per thread or one device per process, the general usage of API remains unchanged from NCCL 1.x to 2.x. Group API is not required in this case.CountsCounts provided as arguments are now of type size_t instead of integer.In-place usage for AllGather and ReduceScatterFor more information, see In-Place Operations in the NCCL Developer Guide. AllGather arguments orderThe AllGather function had its arguments reordered. The prototype changed from:ncclResult_t ncclAllGather(const void* sendbuff, int count,ncclDataType_t datatype,void* recvbuff, ncclComm_t comm, cudaStream_t stream);to:ncclResult_t ncclAllGather(const void* sendbuff, void*recvbuff, size_t sendcount,ncclDataType_t datatype, ncclComm_t comm, cudaStream_tstream);The recvbuff argument has been moved after the sendbuff argument to be consistent with all the other operations.DatatypesNew datatypes have been added in NCCL 2.x. The ones present in NCCL 1.x did not change and are still usable in NCCL 2.x.Error codesError codes have been merged into the ncclInvalidArgument category and have been simplified. A new ncclInvalidUsage code has been created to cover new programming errors.6.1. SupportRegister for the NVIDIA Developer Program to report bugs, issues and make requests for feature enhancements. For more information, see: https:/// developer-program.NVIDIA Collective Communication Library (NCCL)DU-08730-210_v01 | 9NoticeTHE INFORMATION IN THIS GUIDE AND ALL OTHER INFORMATION CONTAINED IN NVIDIA DOCUMENTATION REFERENCED IN THIS GUIDE IS PROVIDED “AS IS.” NVIDIA MAKES NO WARRANTIES, EXPRESSED, IMPLIED, STATUTORY, OR OTHERWISE WITH RESPECT TO THE INFORMATION FOR THE PRODUCT, AND EXPRESSL Y DISCLAIMS ALL IMPLIED WARRANTIES OF NONINFRINGEMENT, MERCHANTABILITY, AND FITNESS FOR A PARTICULAR PURPOSE. Notwithstanding any damages that customer might incur for any reason whatsoever, NVIDIA’s aggregate and cumulative liability towards customer for the product described in this guide shall be limited in accordance with the NVIDIA terms and conditions of sale for the product.THE NVIDIA PRODUCT DESCRIBED IN THIS GUIDE IS NOT FAULT TOLERANT AND IS NOT DESIGNED, MANUFACTURED OR INTENDED FOR USE IN CONNECTION WITH THE DESIGN, CONSTRUCTION, MAINTENANCE, AND/OR OPERATION OF ANY SYSTEM WHERE THE USE OR A FAILURE OF SUCH SYSTEM COULD RESULT IN A SITUATION THAT THREATENS THE SAFETY OF HUMAN LIFE OR SEVERE PHYSICAL HARM OR PROPERTY DAMAGE (INCLUDING, FOR EXAMPLE, USE IN CONNECTION WITH ANY NUCLEAR, AVIONICS, LIFE SUPPORT OR OTHER LIFE CRITICAL APPLICATION). NVIDIA EXPRESSL Y DISCLAIMS ANY EXPRESS OR IMPLIED WARRANTY OF FITNESS FOR SUCH HIGH RISK USES. NVIDIA SHALL NOT BE LIABLE TO CUSTOMER OR ANY THIRD PARTY, IN WHOLE OR IN PART, FOR ANY CLAIMS OR DAMAGES ARISING FROM SUCH HIGH RISK USES.NVIDIA makes no representation or warranty that the product described in this guide will be suitable for any specified use without further testing or modification. T esting of all parameters of each product is not necessarily performed by NVIDIA. It is customer’s sole responsibility to ensure the product is suitable and fit for the application planned by customer and to do the necessary testing for the application in order to avoid a default of the application or the product. Weaknesses in customer’s product designs may affect the quality and reliability of the NVIDIA product and may result in additional or different conditions and/ or requirements beyond those contained in this guide. NVIDIA does not accept any liability related to any default, damage, costs or problem which may be based on or attributable to: (i) the use of the NVIDIA product in any manner that is contrary to this guide, or (ii) customer product designs.Other than the right for customer to use the information in this guide with the product, no other license, either expressed or implied, is hereby granted by NVIDIA under this guide. Reproduction of information in this guide is permissible only if reproduction is approved by NVIDIA in writing, is reproduced without alteration, and is accompanied by all associated conditions, limitations, and notices.TrademarksNVIDIA, the NVIDIA logo, and cuBLAS, CUDA, cuDNN, cuFFT, cuSPARSE, DIGITS, DGX, DGX-1, Jetson, Kepler, NVIDIA Maxwell, NCCL, NVLink, Pascal, T egra, T ensorRT, and T esla are trademarks and/or registered trademarks of NVIDIA Corporation in the Unites States and other countries. Other company and product names may be trademarks of the respective companies with which they are associated.Copyright© 2018 NVIDIA Corporation. All rights reserved.。

Mellanox Quantum QM8700 InfiniBand交换机产品说明说明书

©2020 Mellanox Technologies. All rights reserved.†For illustration only. Actual products may vary.© Copyright 2020. Mellanox, Mellanox logo, Connect-X, MLNX-OS, and UFM are registered trademarks of Mellanox Technologies, Ltd. Mellanox Quantum and Scalable Hierarchical Aggregation and Reduction Protocol (SHARP) are trademarks of Mellanox Technologies, Ltd. All other trademarks are property of their respective owners.page 2Mellanox QM8700 InfiniBand Switch 350 Oakmead Parkway, Suite 100, Sunnyvale, CA 94085Tel: 408-970-3400 • Fax: Table 1 - Part Numbers and Descriptions53779PB Rev 2.2Note : All tall-bracket adapters are shipped with the tall bracket mounted and a short bracket as accessory.Support : For information about our support packages, please contact your Mellanox Technologies sales representative or visit our Support Index page .HDR100QM8700 together with the Mellanox ConnectX ®-6 adapter card support HDR100. By utilizing two pairs of two lanes per port, theQM8700 can support up to 80 ports of 100G to create the densest TOR switch available in the market. This is a perfect solution for double dense racks with more than 40 servers per rack and also helps small-medium deployments with the need to scale to 3-level fat-tree, to lower power, latency and space.MANAGEMENTThe QM8700’s x86 ComEx Broadwell CPU comes with an on-board subnet manager, enabling simple, out-of-the-box bring-up for up to 2K nodes in the fabric. Running the MLNX-OS ® software package, it delivers full chassis management through CLI, WebUI, SNMP or JSON interfaces.QM8700 also incorporates Mellanox’s Unified Fabric Manager (UFM ®) software for managing scale-out, InfiniBand, computing environments to enable efficient provisioning, health indications and monitoring of the cluster. UFM ® ensures that the fabric is up and running at maximum performance at all times.Safety –CB –cTUVus –CE –CUEMC (Emissions) –CE –FCC –VCCI –ICES –RCMOperating Conditions –Temperature:• Operating 0ºC to 40ºC• Non-operating -40ºC to 70ºC –Humidity:• Operating 10% to 85% non-condensing• Non-operating 10% to 90% non-condensing–Altitude: Up to 3200m Acoustic –ISO 7779 –ETS 300 753Others–RoHS compliant –Rack-mountable, 1U –1-year warrantyCOMPLIANCEMellanox QM8700–19’’ rack mountable 1U chassis –40 QSFP56 non-blocking ports with aggregate data throughput up to 16Tb/s (HDR)Switch Specifications–Compliant with IBTA 1.21 and 1.3 –9 virtual lanes: 8 data + 1 management–256 to 4Kbyte MTU –Adaptive Routing –Congestion control –Port Mirroring–VL2VL mapping–4X48K entry linear forwarding databaseManagement Ports–100/1000 RJ45 Ethernet port –RS232 console port –USB port –DHCP–Industry standard CLI –Management over IPv6 –Management IP –SNMP v1,v2,v3 –WebUIFabric Management–On-board Subnet Manager (SM) supporting fabrics of up to 2K nodes –Unified Fabric Manager (UFM) agentConnectors and Cabling–QSFP56 connectors–Passive copper or active fiber cables–Optical modulesIndicators–Per port status LED Link, Activity–System LEDs: System, fans, powersupplies–Unit ID LEDPower Supply–Dual redundant slots –Hot plug operation –Input range:100-127VAC, 200-240VAC –Frequency: 50-60Hz, single phase AC, 4.5A, 2.9A Cooling –Front-to-rear or rear-to-front cooling option–Hot-swappable fan unitPower Consumption –Contact Mellanox Sales FEATURES。

华东师范大学超算中心 - MPI 使用情况说明

华东师范大学超算中心MPI使用情况说明
华东师大超算中心MPI使用情况说明
曙光公司为华东师大提供了丰富的MPI通讯库。

主要有如下几种：
1. INTEL MPI
是INTEL公司提供的高性能MPI，同时支持Infiniband、TCP两种网络，安装路径为/data/soft/compiler/mpi/impi/3.2.2.006
2. HP-MPI
目前是Platform旗下的MPI产品，版本2.2.7，环境变量设定参考
/data/share/env_hpmpi；
3. MV APICH2
是MPI接口在Infiniband网路上的MPI2实现版本，在Infiniband上具有较高的性能。

安装路径为
/data/soft/compiler/mpi/mvapich2/1.4rc2/icc.ifort/
4. OpenMPI
是MPI2实现的高性能的MPI，可以运行于任何网络上。

安装路径为
下面将以cpi为例，介绍不同的MPI利用作业调度系统运行的方法。

5. 作业调度系统上MPI使用举例5.1 INTEL MPI
环境变量设定为：
测试脚本为：
5.2 HP MPI
环境变量设定为：
测试脚本为：
5.3 MVAPICH2
环境变量设定为：
运行脚本为：
5.4 OpenMPI
环境变量设定为：
运行脚本为：。

AIStation 人工智能平台用户指南说明书

Artificial intelligence development platformRelease AI computing power, accelerate intelligent evolutionAI&HPCAIStation -Artificial Intelligence PlatformUser DataUtilizationTraining40%→80%2 days →4 hrs.Telecom FinanceMedicalManuf.Trans.Internet Development & TrainingDeployment & InferenceDeployment2 days →5 minDataModelServingLaptopMobileIndustryRobotIoTPyTorch Caffe MxNetPaddlePaddle TensorFlow ImportPre-processing accelerationTraining Visualization Hyper-para. tuningOn-demandAuto sched.OptimizationJupyter WebShell PipelineData mgnt computing resources Dev. ToolsModelTensorFlow ServingTensorRT Inference Server PyTorch Inference ServerServingDeployingDev. Tools PipelineData processing RecommendationsystemCV NLPScenarioOn-demand Auto sched.Optimization"Efficiency" has become a bottleneck restricting the development of enterprise AI businesspycharmjupyterVstudiosublime70%50%70%Data security issuesInefficient collaborative developmentLack of centralized data management Low resource utilizationInconvenient for large-scale trainingDecentralized Resource Management Lack of synergy in R&D, slow business responseR&D lacks a unified processAIStation –Artificial intelligence development platformTensorflow Pytorch Paddle Caffe MXNetAIStation Integrated development platformModel DevelopmentBuild environment Model Debugging Model OptimizationModel Deployment Model Loading Service DeploymentAPI ServiceModel Training Task Queuing Distributed Training Visual AnalysisAI computing resourcesTraining samplesApplication stackCPU GPUNFS BeeGFS HDFSComputing Resource Pooling User Quota management Utilizing GPU usagePool schedulingData accelerationAutomated DDP trainingSSDResource poolData pooldata1data2data3node1node2node3data4data5Dataset managementData pre-loading Cached data managementSolving data IO bottleneck Accelerating large scale dataset transferring and model trainingLow threshold for DDP training Helping developers drive massive computing power to iteratively trainmodelsbatch2batch1batch0Data loadingbatch3BeeGFSwork2GPU serverworker1GPUserverworker0GPUserverwork3GPU ServerAIStation TensorFlowCustomized MPI operatorsHighlighted featuresSSDSSDGPU GPU GPU GPU GPUGPUGPUGPUGPU Cards MIG instancesResource PoolingUser QuotaUser QuotaA I St a t i o n d e ve l o p m e n t P l a t f o r m A rc h i te c t u reP100V100V100sA100A30… …Ethernet ClusterInfiniband ClusterRoCE ClusterStorageNFS 、BeeGFS 、Lustre 、HDFS 、Object StorageLinux OSNVIDIA driver package: GPU, Mallanox NIC, etcOperating SystemHardware ClusterNVIDIAGPU seriesMonitoringSchedulingGPU PluginOperatorKubernetes + dockerNetwork PluginSRIOV PluginMultus CNIData prep.Algorithm prototype TrainingTestResource Enginedata mgmtJupyterimage mgmtwebshell/ssh multi-instance visualizationquota mgmtresource mgmt deployment job workflowmgmt job lifecycleproject mgmtalgorithm mgmtmodel mgmt Report HAMulti-tenant System settingBusiness ManagementAuthenticationAPIsAI Application Development3rd or user-defined system integrationDeployment ModeComputing Nodes Storage ：SSD 2T-10TGPU ：8*V100Management network Ethernet @ 1/10Gbps IPMIEthernet @ 1GbpsManagement Node Storage size ：4T-100TCluster Size （10-80persons ）ManagerDeployment Mode （Larger Scale+HA ）Storage 100T-200TManagement network Ethernet @ 1/10Gbps IPMIEthernet @ 1Gbps Management Node 1*Main ，2*BackupCluster Size （10-80persons ）Computing NodesSSD 2T-10T 8*V100Computing NodesSSD 2T-10T 8*V100Manager...I00G EDR infiniband EDR@100GpsOne-stop service full-cycle management,Easy use for distributed trainingHelping developers drive massive computing powerto iteratively train modelsOne-stop AI Dev. platformAI framework AI ops tools GPU driver & Cuda GPUStandard interface for AI Chips Multiply AI Chips supportedHeterogeneousComprehensive resource using statisticsData security and access control Automatic faulty analysis and solutionsIntelligent maintenance & securityHighlighted featuresAIStationStandard and unifiedManagementPollingSchedulingCPU GPU FPGAASICA100A30A40V100MLU270MLU390Cloud AIC 100•Personal data isolation•Collaborative sharing of public data •Unified data set managementC e n t r a l i z e d d a t a m a n a g e m e n tf a c i l i t a t e c o l l a b o r a t i v e d e v e l o p m e n t •Dataset preloading •Data Affinity Scheduling•Local cache management strategyD a t a c a c h e a c c e l e r a t i o ne f f e c t i v e l y s o l v e I /O b o t t l e n e c k s AIStation –Data Synergy Acceleration•Data access control•Data security sandbox, anti-download •Multiple copies ensure secure data backupS e c u r i t y p o l i c yUser DataTraining SamplesSharing Data（NFS 、HDFS 、BeeGFS 、Cloud Storage ）D a t a M a n a g e m e n t ：M u l t i -s t o r a g e Sy s t e m•Support “main -node ”storage using mode ；•Unified access and data usage for NFS 、BeeGFS 、HDFS 、Lustre through UI;•Built-in NFS storage supports small file merger and transfer, optimizing the cache efficiency of massive small filesAIStationComputing PoolStorage extension （storage interface 、data exchange ）Data accelerationMain storageSSD+BeeGFSNode Storage（NFS ）Node Storage（HDFS ）Node Storage（Lustre ）Data exchangeGPU PoolAIStationUser01UserNcaffeTensorflowmxnetpytorchGPUGPU GPU GPU GPUGPUGPUGPUGPU GPU GPU GPU GPUGPUGPUGPUGPU GPU GPU GPU GPUGPUGPUGPUGPU GPU GPU GPU GPUGPUGPUGPUAIStation –Resource SchedulingR e s o u r c e a l l o c a t i o n m a n a g e m e n tUser GPU resource quota limit User storage quota limitResource partition: target users, resource usageF l e x i b l e r e s o u r c e s c h e d u l i n g•Network topology affinity scheduling •PCIE affinity scheduling•Device type affinity scheduling •GPU fine-grained distributionD y n a m i c s c h e d u l i n g•Allocate computing resources on demand •Automatically released when task completedG P U M a n a g e m e n t ：F i n e g r a n u l a r i t y G P U u s i n guser1user2user3user4user2481632123456GPU mem （G ）Time （H ）user1user2IdleIdle481632123456GPU mem （G ）Time （H ）GPU sharing scheduling policy based on CUDA to realize single-card GPU resource reuse and greatly improve computing resource utilization.Elastic sharing:Resources are allocated based on the number of tasks to be multiplexed.A single card supports a maximum of 64tasks to be multiplexed.Strict sharing:the GPU memory is isolated and allocated in any granularity (minimum:1GB).and resources are isolated based on thegraphics memory ；Flexible and convenient:user application to achieve "zero intrusion",easy application code migration ；S c h e d u l i n g w i t h M I G8 * A100 GPUsN V I D I A A100M I G s u p p o r t i n gUtilizing GPU usage• A single A100 GPU achieves up to 7x instance partitioning and up to56x performance on 8*A100 GPUs in Inspur NF5488A5;•Allocates appropriate computing power resources to tasks withdifferent load requirements.•Automatic MIG resource pool management, on-demand application,release after use;Convenient operation and maintenance•Set different sizes of pre-configured MIG instance templates.•Standard configuration UI for IT and DevOps team.•Simplify NVIDIA A100 utilization and resource management;56 *MIG instancesRe s o u rc e m a n a g e m e n t ：N U M A ba s e d s c h e d u l i n gKubeletResource management PluginInspur-DevicePluginGPUGPU topo scoreGPU resource updateGPU allocatingAIStation SchedulerGPU allocationAutomatically detects the topology of compute nodes and preferentially allocates CPU and GPU resources in the same NUMA group to a container to make full use of the communication bandwidth in the groupAIStation –Integrated AI training frameworkPrivate image library PublicimagelibraryinspurAIimagelibraryAI DevelopmentFrameworkAI Developmentcomponents and toolsGPU Driver anddevelopment libraryGPU computingresources◆Te n s o r f l o w,P y t o r c h,P a d d l e,C a f f e,M X N e t◆B u i l d a p r i v a t e w a r e h o u s e t o m a n a g et h e A I a p p l i c a t i o n s t a c k◆S u p p o r t i m a g e c r e a t i o n a n d e x t e r n a li m p o r t◆S u p p o r t o p e n s o u r c e r e p o s i t o r i e s s u c ha s N G C a n d D o c k e r H u b◆B u i l t-i n m a i n s t r e a m d e v e l o p m e n t t o o l sa n d s u p p o r t d o c k i n g w i t h l o c a l I D E•Built-in Jupyter and Shell tools •Support docking with local IDE •Support command line operationQuickly enterdevelopment mode•Allocate computing resources on demand•Quick creation through the interface•Rapid Copy Development EnvironmentRapid build Model Development Environment•Life cycle management •Real-time monitoring of resource performance•One-click submission of training tasksCentralized management of development environmentQuickly build development environment, focus on model developmentD e ve l o p m e n t P l a t f o r mJupyterWebShell本地IDEDevelopment PlatformDev. Platform StatusDevelopment environment instancemonitoring The development environment saves the imageS e c o n d l e v e l b u i l d•On –demand GPU ；•T ensorflow/MXNet/Pytorch/Caffe ；•Single-GPU, multi-GPU, distributed training ；•Flexible adjustment of resources on demand decouples the binding of runtime environment and computing power ；I n t e r a c t i v e m o d e l i n g •Jupyter / WebShell / IDE V i s u a l i z a t i o nT ensorBoard / Visdom / NetscopeF u l l c y c l e m a n a g e m e n t S t a t u smonitoring/Performance monitoring/Port password memoryImage save/copy expansion/start/delete etcVisualizationTensorboardVisdom NetscopeEnhanced affinity scheduling, optimized distributed scheduling strategy, multi-GPU training acceleration ratio can reach more than 90%.Optimized most of the code based on open source;Fixed a bug where workers and launchers could not start at the same time;Task status is more detailed.•Supports distributed training for mainstream frameworks•Provides one-page submission and command line submission of training tasks.M u l t i p l e w a y s t o s u p p o r t d i s t r i b u t e dQ u i c k s t a r t d i s t r i b u t i o nI m p r o v e c o m p u t i n g p e r f o r m a n c eDistributed task scheduling to speed up model trainingAIStation –Training ManagementAIStation –Resource MonitoringO v e r a l l M o n i t o r i n g•Usage status of cluster resources such as GPU, CPU, and storage •Computing node health and performance•User task status and resource usageR e s o u r c e U s a g e St a t i s t i c s•Cluster-level resource usage statistics•Cluster-level task scale statistics•User-level resource usage statistics•User-level task scale statisticsS y s t e m A l a r m•hardware malfunction•System health status•Computing resource utilizationM u l t i -te n a n t M a n a g e m e n tAIStationUserUser2User group1User group2Kubernetes Namespace1Namespace2Cluster resource ☐Supports an administrator -Tenant administrator -Common User organization structure. Tenant administrators can conveniently manage user members and services in user groups, while platformadministrators can restrict access to and use of resources and data in user groups.☐User authentication: LDAP as user authentication system, supporting third-party LDAP/NIS systems.☐Resource quotas control for users and user groups using K8S namespace.☐User operations: Users can be added, logged out, removed, and reset passwords in batches. Users can be enabled or disabled to download data and schedule urgent tasks.I n t e l l i g e n t p l a t f o r m o p e r a t i o n a n d m a i n t e n a n c eIntelligent diagnosis and recovery tool•Based on the existing cluster monitoring, alarm and statistics functions, the operation monitoring component is removed to support independent deployment and use;•Health monitoring: Obtain the status and list display (monitoring information and abnormal events display) of components (micro-services and NFS).•Abnormal repair: Based on the operation and maintenance experience of AIStation, automatic or manual repair of the sorted events such as interface timeout and service abnormalities (microservice restart and NFS remount);Intelligent fault toleranceSupports active and standby management node health monitoring, HA status monitoring, and smooth switchover between active and standby management nodes without affecting services. Monitors alarms forabnormal computenode resource usage toensure the smoothrunning of computenodes.In the event of a systemfailure, the training taskautomatically startssmooth migrationwithin 30 secondsMonitor the status ofkey services andabnormal warning toensure the smoothoperation of user coreservices.M a n a g e m e n t n o d e h i g h l y a v a i l a b l e C o m p u t i n g n o d eF a u l t t o l e r a n c eC r i t i c a l s e r v i c e sf a u l t t o l e r a n tTr a i n i n g m i s s i o nf a u l t t o l e r a n c eN o r t h b o u n d i n t e r f a c e•Secure, flexible, extensible northbound interface based on REST APIs.AIStationQuery URL Status Usages Performance status logs performance resultsReturn URL resource framework scripts dataset environment Login info performance resource framework dataset Return URL Query URL Query URL Return URL monitordeveloping training Computing resourcesDatasets Applications Caffe TensorFlow Pytorch Keras MXNet theanodata1data2data3data4data5AIStation product featuresFull AI business process support Integrated cluster management Efficient computing resource scheduling Data caching strategy reliable security mechanismsUse Case ：Automatic driveSolutions:•Increasing computing cluster resource utilization by 30% with efficient scheduler.•One-stop service full-cycle management,streamlined deployments.•Computing support, data management.Background ：•Widely serving the commercial vehicle and passenger vehiclefront loading market. •The company provides ADAS and ADS system products andsolutions, as well as high-performance intelligent visualperception system products required for intelligent driving.U s e C a s e ：c o m m u n i c a t i o n s te c h n o l o g y c o m pa n y•Quick deployment and distributed •GPU pooling •Huge files reading and training optimizationBackground•HD video conference and mobile conference are provided,and voice recognition and visual processing are the main scenarios.•Increased scale of sample data,distributed training deployment and management,a unified AI development platform is required to support the rapid development of service.ProblemsSolutions •Increasing size of dataset (~1.5T), distributed training;•GPU resource allocating automatically ；•Efficient and optimized management for the huge set of small files ；Use Case: Build One-Stop AI Workflow for Largest Excavator Manufacturer Revenue 15.7B$ExcavatorsPer Year 100,000+Factories 30+AIStation built one-stop AI workflow to connect cloud, edge,and localclusters; support 75 production systems.API Calls Per day 25 M QoS 0missper 10M calls Model Dev Cycle 2 weeks -> 3days Use AI to automate 90% production lines, double production capacity.SANY HEAVY INDUSTRY CO., LTDSANY CloudAIStationModel Dev &Training Inference ServiceSensor Data Data Download Realtime work condition analysis Inference API invoke Training Cluster Inference ClusterTraining Jobs InferenceServices200 * 5280M5 800 * T4, inference; 40* 5468M5 320 * V100, training。

Fluent的并行计算

Possibilities of Parallel Calculations in Solving Gas DynamicsProblems in the CFD Environment of FLUENT SoftwareN. Chebotarev Research Institute of Mathematics and Mechanics, Kazan StateUniversity, Kazan, RussiaReceived August 25, 2008Abstract：The results of studying an incompressible gas flow field in a periodic element of the porous structure made up of the same radius spheres are presented; the studies were based on the solution of the Navier–Stokes equations using FLUENT software. The possibilities to accelerate the solution process with the use of parallel calculations are investigated and the calculation results under changes of pressure differential in the periodic element are given.DOI : 10.3103/S1068799809010103Multiprocessor computers that make it possible to realize the parallel calculation algorithms are in recent use for scientific and engineering calculations. One of the fields in which application of parallel calculations must facilitate a considerable progress is the solution of three-dimensional problems of fluid mechanics. Many investigators use standard commercial CFD software that provide fast and convenient solutions of three-dimensional problems in complicated fields.The present-day CFD packages intended to solve the Navier–Stokes equations describing flows in the arbitrary regions contain possibilities of parallel processing. The objective of this paper is to test the solutions of a three-dimensional problem of gas dynamics using FLUENT software [1] by a multiprocessor computer in the mode of parallel processing.An incompressible gas flow in the porous structure made up of closely-arranged spheres is calculated. Structures of different sphere arrangement are widely used as models of porous media in the theory of filtration. Using the porous elements it is possible to realize the processes of filtration, phase separation, throttling, including those in aircraft engineering [2]. The hydrodynamic flows in porous structures in the domain of small Reynolds numbers are described, as a rule, under the Stokesapproximation without regard for the inertia terms in the equations of fluid motion [3]. At the same time, the flow velocities in the porous media may be rather large, and the Stokes approximation will not describe a real flow pattern. In this case the solution of the complete Navier–Stokes equations should be invoked. The flow with regard for inertia terms in the equations of fluid motion in the structures of different sphere arrangement was theoretically studied in [6] and experimentally in [7].PROBLEM STATEMENTWe consider an incompressible gas flow in the three-dimensional periodic element of the porous structure made up of closely-arranged spheres of the same diameter d , the centers of which are in the nodes of the ordered grid (Fig. 1a). The porosity of the structure under consideration determined as the ratio of the space occupied by the medium to the total volume is equal to 0.26. Taking into account symmetry and periodicity of the flow, we will separate in the space between spheres the least element of the region occupied by air (Fig. 1b). In connection with a difficulty in dividing the calculation domain, small cylindrical areas are excluded in the vicinity of points at which the element spheres are in contact.Fig. 1. Scheme of sphere arrangement (a) and a periodic element in the air space between spheres(b).The gas flow velocities inside the porous structures are so small that it is possible to neglect gas compressibility and adopt a model of incompressible fluid. The laminar flow of the incompressible gas is described by the stationary Navier–Stokes equations:where u are the gas velocity vector and its Cartesian components; p is the pressure; μ andρ are the dynamic viscosity coefficient and air density. At the end bounds of the periodic element we lay down the conditions of periodicitywhere L = d is the periodic element length along the flow (along the y axis); at the lateral faces we lay down the conditions of symmetry. The pressure at the end element bounds is described by the formulawhere Δp is the pressure differential in the element limits. The conditions of symmetry are taken not only at the lateral faces but also at the upper and lower faces. On the spherical surfaces the conditions of adhesion are specified.System of equations (1)–(2) is solved with the aid of the SIMPLE algorithm in the finite volume method in FLUENT software environment (FLUENT 6.3.26 version). For the calculation domain the irregular tetrahedral grid division is used (Fig.2).Fig. 2. Division of the periodic element into finite volumes.ANAL YSIS OF CALCULATION RESULTS IN THE PARALLEL MODEThe calculations were carried out on the computational cluster of Kazan State University consisting of eight servers. Each server includes two AMD Opteron 224 processors with the clock rate 1.6 GHz and 2 GB of main memory. The servers operate under the control of the Ubuntu 7.10 version of the Linux operating system. The communication between the servers is based on the Gigabit Enternet technology. In the calculations the HP Message Passing Interface library (HP-MPI) deliveredtogether with the FLUENT program is used. At the moment of experiments four servers were accessible for operation. To analyze the efficiency of parallel processing of the numerical solutions of the Navier–Stokesequations in the FLUENT environment, the calculations were performed with three variants of the grid division of the solution domain 116895, 307946, and 510889 finite volumes (variants A , B , C ). The number of iterations in all cases was taken to be equal to 760 resulting in solution convergence to10. All computation experiments were performed in the package mode.One of the basic moments in solving problem with the use of FLUENT software in the parallel processing mode is the division of the initial domain into subdomains. In this case, each computation unit, that is, a processor is responsible for its subdomain. In dividing into subdomains FLUENT software uses the method of bisection, that is, when it is necessary to divide into four subdomains, the initial domain is first divided into two and then recursively the daughter subdomains are divided into two. If it is necessary to divide into three subdomains, the initial domain is divided into two subdomains so that one subdomain is twice as large as the other and then the larger subdomain is divided into two. FLUENT software incorporates several algorithms of bisection, and the efficiency of each algorithm depends on the problem geometry.We studied the acceleration factor that is determined as the ratio of the calculation time t1 by one processor to the calculation time t n by n processors k a .To provide the experiment purity, the acceleration factor was calculated four times for each case, and the calculation time obtained in each case was somewhat different. The minimal time for four calculations was chosen for the analysis. For the variants A , B , C without paralleling it amounted to 747, 2234, and 3600 s, respectively.The dependence of k a on the number of n processors being used is given in Fig.3. If the number of processors is small ( n < 5), the acceleration factor is about the same for all variants of the grid division and is close to the number n . As n increases, the parameter k a becomes much less than the number of processors and becomes different for different variants of the grid division. As a whole, the acceleration factor behavior with changes in the number of processors is in conformity with the theoretical concepts, and k a tends to the final limiting value as n grows. For the variant A with a lesser number of finite volumes, the efficiency of paralleling is lowerthan for the variants B and C .Fig. 3. Dependence of the acceleration factor on the number of processors.For a more detailed analysis of the calculation time with the different number of processors, we study the time t of data exchange between the processors and the factor t n of unloaded state [8]that represents the share of exchange time in the total calculation time. The less is k u , the higher is the efficiency of paralleling. The time of data exchange is determined by a share of boundary finite volumes between subdomains in the total number of finite volumes. Tables 1 and 2 present a share of boundary cells between subdomains and the values of the factor of unloaded state. It is seen that as the number of processors grows, the share of boundary volumes between subdomains increases resulting in the growth of the relative exchange time t u , that is, the factor of unloaded state. In this case, it is clear that the variants B and C are close to each other in both variants. In the variant A , the ratio of the exchange time to the calculation time increases much faster.The flow pattern in the element under study is mainly determined by the value of pressure differential along the fluid flow. Denoting by v0 the average velocity in theinlet cross-section, we will introduce the Reynolds number Re:At low velocities the gas flow in the porous structure is described by the Darcylaw:widely used [9]:Fig. 4. V ector field of velocities at the periodic element faces at x = 0 (a, c) and x = d /2 (b, d) forRe = 0.05 (a, b) and Re = 140 (c, d).The permeability factor k d and the coefficient β are usually found by approximation of experimental results. At the same time the numerical solution of the equations for gas flow in the periodic element of the porous structure can be used for their definition. Let us write the equation for momentum variation when gas passes through the periodic element in the form [6]:where n is the unit vector that is exterior with respect to the boundary element surfaceA f; τ is the vector of viscous stresses. The boundary surface of the element consists of the bo undary “fluid–fluid”A ff，the boundary “fluid–solid” A fs：A f=A ff+A fs Let us rewrite Eq. (8) with regard for symmetry in the form:where A is the area of the inlet cross-section in the periodic element. Taking into account that in connection with flow periodicity the last integral is zero, we will write Eq. (9) in the dimensionless form:where we introduced the dimensionless parameter λ= d2Δp/ρvL.The gas velocity distribution in the porous element found from the solution of the Navier–Stokes equations makes it possible to calculate the forces acting on the spherical surfaces in the element limits, that is, the integrals in the right-hand part ofEq. (10). The dimensionless force of resistance λRe includes the resistance that is due to the normal f p and viscous f τ stresses on the spheres. Figure 5 presents the dependence of Re λ on the Reynolds number obtained from the calculations of the gas flow field at different values of pressure differential (calculated points), and the curve found by linear approx imation of the calculated values Re λ at Re 4 > . The linear approximation obtained corresponds to the Forchheimer formula and is in a good agreement with the calculated data thus confirming the feasibility of the formula mentioned for description of flow characteristics in the porous media at the large Reynolds numbers. Using formulas (7) and (10), we will obtain from the calculated data thepermeability factor k d/d2=6.1 × 10-4.Fig. 5. Dependence of the dimensionless resistance force on the Reynolds number.Thus, using FLUENT software in the parallel calculations mode by a multi-processor computer, we solved a problem of the incompressible gas flow in the periodic element of the porous structure made up of the closely-arranged spheres. It is shown that the calculation time is reduced as the number of processors grows, and the efficiency of paralleling is higher with a larger number of finite volumes in dividing the calculation domain. The results of studying a flow field under variation of the pressure differential in the periodic element are presented. When the pressure differential increases (the large Reynolds numbers), the inertia flow regime that is characterized by a complicated pattern of vortex structures is formed in the porous element.。

Introduction MPI- The Message Passing Interface Library

MPI - The Message Passing Interface LibraryIntroductionMPI is a message passing interface used for parallel processing in distributed memory systems. There are language specific bindings that allow programs written in C, C++, FORTRAN77 and FORTRAN90 to execute in parallel by calling the appropriate library routines.A single user program is prepared, but is run on multiple processes. Each instance of the program is assigned a unique process identifier, so that it "knows" which process it is. This allows the same program to be executed, but for different things to happen in each process. Frequently, the user sets up one process as a master, and the others as workers, but this is not necessary. Each process has its own set of data, and can communicate directly with other processes to pass data around.Because the data is distributed, it is likely that a computation on one process will require that a data value be copied from another process. Thus, if process A needs the value of data item X that is stored in the memory of process B, then the program must include lines that say something like:if ( I am processor A ) thencall MPI_Send ( X )else if ( I am processor B ) thencall MPI_Recv ( X )endIt should be clear that a program using MPI to execute in parallel will look much different from a corresponding sequential version. The user must divide the problem data among the different processes, rewrite the algorithm to divide up work among the processes, and add explicit calls to transfer values as needed from the process where a data item "lives" to a process that needs that value.Using MPI with C or C++A C or C++ file that uses MPI routines or constants must include the lineinclude "mpi.h"Here is a sample C program that uses MPI:#include < stdio.h >#include < stdlib.h >#include "mpi.h"int main ( int argc, char *argv[] ){int ierr;int master = 0;int my_id;int num_procs;/*Initialize MPI.*/ierr = MPI_Init ( &argc, &argv );/*Get the number of processes.*/ierr = MPI_Comm_size ( MPI_COMM_WORLD, &num_procs );/*Get the individual process ID.*/ierr = MPI_Comm_rank ( MPI_COMM_WORLD, &my_id );/*Print a message.*/if ( my_id == master ){printf ( "\n" );printf ( "HELLO_WORLD - Master process:\n" );printf ( " A simple C program using MPI.\n" );printf ( "\n" );printf ( " The number of processes is %d\n", num_procs );}printf ( "\n" );printf ( " Process %d says 'Hello, world!'\n", my_id );/*Shut down MPI.*/ierr = MPI_Finalize ( );return 0;}Using MPI with FortranA FORTRAN77 program, subroutine or function that uses MPI must include the lineinclude "mpif.h"which defines certain parameters and function interfaces.A FORTRAN90 program, subroutine or function may, instead of an include statement, access the MPI library by the lineuse mpiHowever, this does not seem to be acceptable to the current IBM Fortran compilers. Thus, for now, FORTRAN90 programs on the IBM systems will need to continue to use the old style include statement.Here is a sample FORTRAN90 program that uses MPI:program helloinclude 'mpif.h'integer errorinteger, parameter :: master = 0integer num_procsinteger world_id!! Initialize MPI.!call MPI_Init ( error )!! Get the number of processes.!call MPI_Comm_size ( MPI_COMM_WORLD, num_procs, error )!! Get the individual process ID.!call MPI_Comm_rank ( MPI_COMM_WORLD, world_id, error )!! Print a message.!if ( world_id == master ) thenprint *, ' 'print *, 'HELLO_WORLD - Master process:'print *, ' A simple FORTRAN 90 program using MPI.'print *, ' 'print *, ' The number of processes is ', num_procsend ifprint *, ' 'print *, ' Process ', world_id, ' says "Hello, world!"'!! Shut down MPI.!call MPI_Finalize ( error )stopendReferences:•Parallel Programming with MPI,Peter Pacheco,Morgan Kaufman, 1996.•High Performance Computing and the Art of Parallel Programming: an Introduction for Geographers, Social Scientists, and Engineers,Stan Openshaw and Ian Turton,Routledge, 2000.•Using MPI: Portable Parallel Programming with the Message-Passing Interface, William Gropp, Ewing Lusk, Anthony Skjellum,Second Edition, MIT Press, 1999,Available (to FSU users) as an online e-book.•Using MPI-2: Advanced Features of the Message-Passing Interface, William Gropp, Ewing Lusk, Rajiv Thakur,Second Edition, MIT Press, 1999,Available (to FSU users) as an online e-book.•The MPI web site at Argonne National Lab: /mpi/•MPI: The Complete Reference,Volume I: The MPI Core,Marc Snir, Steve Otto, Steven Huss-Lederman, David Walker, Jack Dongarra,Second Edition, MIT Press, 1998,Available (to FSU users) as an online e-book.•MPI: The Complete Reference,Volume II: The MPI-2 Extensions,William Gropp, Steven Huss-Lederman, Andrew Lumsdaine, Ewing Lusk, Bill Nitzberg, William Saphir, Marc Snir,Second Edition, MIT Press, 1998,Available (to FSU users) as an online e-book.•MPI: A Message Passing Interface Standard,The Message Passing Interface Forum, 1995,Available online from the MPI Forum.•MPI-2: Extensions to the Message Passing Interface,The Message Passing Interface Forum, 1997,Available online from the MPI Forum.•RS/600 SP: Practical MPI Programming,Scott Vetter, Yukiya Aoyama, Jun Nakano,IBM Redbooks, 1999ISBN: 0738413658Available online from IBM Redbooks.Source: /supercomputer/sp3_mpi.html。

Intel oneAPI Collective Communications Library 开发者

Get Started with Intel® oneAPI Collective Communications LibraryGet Started with Intel® oneAPI Collective Communications Library ContentsChapter 1: Get Started with Intel® oneAPI Collective Communications Library2Get Started with Intel® oneAPI Collective Communications Library 1Intel® oneAPI Collective Communications Library (oneCCL) is a scalable and high-performance communication library for Deep Learning (DL) and Machine Learning (ML) workloads. It develops the ideas originated in Intel(R) Machine Learning Scaling Library and expands the design and API to encompass new features and use cases.Before You BeginBefore you start using oneCCL, make sure to set up the library environment. There are two ways to set up the environment:•Using standalone oneCCL package installed into <ccl_install_dir>:source <ccl_install_dir>/env/setvars.sh•Using oneCCL from Intel® oneAPI Base Toolkit installed into <toolkit_install_dir> (/opt/intel/ inteloneapi by default):source <toolkit_install_dir>/setvars.shSystem RequirementsRefer to the oneCCL System Requirements page.Sample ApplicationThe sample code below shows how to use oneCCL API to perform allreduce communication for SYCL USM memory.#include <iostream>#include <mpi.h>#include "oneapi/ccl.hpp"void mpi_finalize() {int is_finalized = 0;MPI_Finalized(&is_finalized);if (!is_finalized) {MPI_Finalize();}}int main(int argc, char* argv[]) {constexpr size_t count = 10 * 1024 * 1024;int size = 0;int rank = 0;ccl::init();MPI_Init(nullptr, nullptr);MPI_Comm_size(MPI_COMM_WORLD, &size);MPI_Comm_rank(MPI_COMM_WORLD, &rank);31 Get Started with Intel® oneAPI Collective Communications Libraryatexit(mpi_finalize);sycl::default_selector device_selector;sycl::queue q(device_selector);std::cout << "Running on " << q.get_device().get_info<sycl::info::device::name>() << "\n"; /* create kvs */ccl::shared_ptr_class<ccl::kvs> kvs;ccl::kvs::address_type main_addr;if (rank == 0) {kvs = ccl::create_main_kvs();main_addr = kvs->get_address();MPI_Bcast((void*)main_addr.data(), main_addr.size(), MPI_BYTE, 0, MPI_COMM_WORLD);}else {MPI_Bcast((void*)main_addr.data(), main_addr.size(), MPI_BYTE, 0, MPI_COMM_WORLD);kvs = ccl::create_kvs(main_addr);}/* create communicator */auto dev = ccl::create_device(q.get_device());auto ctx = ccl::create_context(q.get_context());auto comm = ccl::create_communicator(size, rank, dev, ctx, kvs);/* create stream */auto stream = ccl::create_stream(q);/* create buffers */auto send_buf = sycl::malloc_device<int>(count, q);auto recv_buf = sycl::malloc_device<int>(count, q);/* open buffers and modify them on the device side */auto e = q.submit([&](auto& h) {h.parallel_for(count, [=](auto id) {send_buf[id] = rank + id + 1;recv_buf[id] = -1;});});int check_sum = 0;for (int i = 1; i <= size; ++i) {check_sum += i;}/* do not wait completion of kernel and provide it as dependency for operation */std::vector<ccl::event> deps;deps.push_back(ccl::create_event(e));/* invoke allreduce */auto attr = ccl::create_operation_attr<ccl::allreduce_attr>();ccl::allreduce(send_buf, recv_buf, count, ccl::reduction::sum, comm, stream, attr, deps).wait();/* open recv_buf and check its correctness on the device side */sycl::buffer<int> check_buf(count);q.submit([&](auto& h) {sycl::accessor check_buf_acc(check_buf, h, sycl::write_only);4Get Started with Intel® oneAPI Collective Communications Library 1 h.parallel_for(count, [=](auto id) {if (recv_buf[id] != static_cast<int>(check_sum + size * id)) {check_buf_acc[id] = -1;}});});q.wait_and_throw();/* print out the result of the test on the host side */{sycl::host_accessor check_buf_acc(check_buf, sycl::read_only);size_t i;for (i = 0; i < count; i++) {if (check_buf_acc[i] == -1) {std::cout << "FAILED\n";break;}}if (i == count) {std::cout << "PASSED\n";}}sycl::free(send_buf, q);sycl::free(recv_buf, q);}Prerequisites•oneCCL with SYCL support is installed and oneCCL environment is set up (see installation instructions)•Intel® MPI Library is installed and MPI environment is set upRun the samplee the C++ driver with the -fsycl option to build the sample:•Linux* OSicpx -fsycl -o sample sample.cpp -lccl -lmpi•Windows* OSmpiexec <parameters> ./samplewhere <parameters> represents optional mpiexec parameters such as node count, processes per node, hosts, and so on.Compile and build applications with pkg-configThe pkg-config tool is widely used to simplify building software with library dependencies. It provides command line options for compiling and linking applications to a library. Intel® oneAPI Collective Communications Library provides pkg-config metadata files for this tool starting with the oneCCL 2021.4 release.The oneCCL pkg-config metadata files cover both configurations of oneCCL: with and without SYCL support.51 Get Started with Intel® oneAPI Collective Communications LibrarySet up the environmentSet up the environment before using the pkg-config tool. To do this, use one of the following options (commands are given for a Linux install to the standard /opt/intel/oneapi location):•Intel(R) oneAPI Base Toolkit setvars.sh script:source /opt/intel/oneapi/setvars.sh•oneCCL setvars.sh script (the prerequisites for this option are listed below):source /opt/intel/oneapi/ccl/latest/env/setvars.shPrerequisites for the setup with oneCCL setvars.shTo set up the environment with oneCCL setvars.sh script, you have to install additional dependencies in the environment:•Intel® MPI Library (for both configurations of oneCCL: with and without SYCL support)•Intel® oneAPI DPC++/C++ Compiler for oneCCL with SYCL supportCompile a program using pkg-configTo compile a test sample.cpp program with oneCCL, run:icpx -o sample sample.cpp $(pkg-config --libs --cflags ccl-cpu_gpu_icpx)--cflags provides the include path to the API directory:pkg-config --cflags ccl-cpu_gpu_icpxThe output:-I/opt/intel/oneapi/mpi/latest/lib/pkgconfig/../..//include/ -I/opt/intel/oneapi/ccl/latest/lib/ pkgconfig/../..//include/cpu_gpu_icpx--libs provides the oneCCL library name, all other dependencies (such as SYCL and MPI), and the search path to find it:pkg-config --libs ccl-cpu_gpu_icpxThe output:-L/opt/intel/oneapi/mpi/latest/lib/pkgconfig/../..//lib/ -L/opt/intel/oneapi/mpi/latest/lib/ pkgconfig/../..//lib/release/ -L/opt/intel/oneapi/ccl/latest/lib/pkgconfig/../..//lib/cpu_gpu_icpx -lccl -lsycl -lmpi -lmpicxx -lmpifortFind More•oneCCL Code Samples•oneCCL Developer Guide and ReferenceNotices and DisclaimersIntel technologies may require enabled hardware, software or service activation.No product or component can be absolutely secure.Your costs and results may vary.© Intel Corporation. Intel, the Intel logo, and other Intel marks are trademarks of Intel Corporation or its subsidiaries. Other names and brands may be claimed as the property of others.No license (express or implied, by estoppel or otherwise) to any intellectual property rights is granted by this document.6Get Started with Intel® oneAPI Collective Communications Library 1 The products described may contain design defects or errors known as errata which may cause the product to deviate from published specifications. Current characterized errata are available on request.Intel disclaims all express and implied warranties, including without limitation, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement, as well as any warranty arising from course of performance, course of dealing, or usage in trade.7。

现代通讯工具的使用及看法作文英语

现代通讯工具的使用及看法作文英语The Evolution and Impact of Modern Communication Tools.In today's era, the world has shrunk to a global village, thanks to the remarkable advancements in modern communication tools. These tools have revolutionized the way we interact, exchange information, and stay connected with each other, regardless of geographical boundaries. The profound impact of these technological marvels is felt across all aspects of life, from personal relationships to global businesses.The advent of the internet marked a significant milestone in the history of communication. It brought about a paradigm shift, allowing people to access a vast repository of knowledge and stay connected with friends and family across the globe. Social media platforms like Facebook, Twitter, and Instagram have furtherrevolutionized the way we socialize and share our lives with others. These platforms have made it possible to stayupdated with the latest news, trends, and opinions from around the world, all in the palm of our hands.Moreover, messaging applications such as WhatsApp, Messenger, and WeChat have transformed the way we communicate. These applications not only allow us to send text messages but also make video calls, share files, and make payments seamlessly. The convenience and accessibility of these tools have made them an integral part of our daily lives.Smartphones, another remarkable invention of modern technology, have further enhanced the communication experience. With a smartphone, we can access the internet, make calls, send messages, watch videos, and play games,all from a single device. The integration of advanced features like GPS, cameras, and sensors has made smartphones a comprehensive communication and entertainment hub.However, the rise of modern communication tools has not been without its challenges. One of the major concerns isthe issue of privacy. With so much personal information being shared online, the risk of privacy breaches and cybercrimes has increased significantly. It is, therefore, crucial to be vigilant and take necessary precautions to protect our personal information.Another challenge is the issue of misinformation and fake news. The ease with which information can be shared online has led to the proliferation of false and misleading content. This has the potential to cause widespread confusion, panic, and even social unrest. It is, therefore, important to verify the authenticity of the information we encounter online before sharing it.Despite these challenges, the benefits of modern communication tools far outweigh the drawbacks. They have broken down barriers, connected people, and facilitated the exchange of ideas and knowledge. They have enabled us to stay connected with our loved ones, even when they are thousands of miles away. They have made it possible for businesses to operate globally, expand their reach, and connect with customers seamlessly.In conclusion, modern communication tools have revolutionized the way we live and interact with each other. They have brought about remarkable advancements in technology, making it easier for us to stay connected, exchange information, and access knowledge from anywhere in the world. While there are challenges and concerns associated with their use, it is important to recognizetheir value and make the most of them while being responsible and vigilant. As we continue to embrace these technological advancements, it is exciting to imagine what the future holds for the world of communication.。

ai在通讯的应用英语作文

ai在通讯的应用英语作文Title: The Application of AI in CommunicationsIn the digital age, Artificial Intelligence (AI) has revolutionized various industries, and its impact on the field of communications is no exception. AI's integration into communication systems has not only enhanced the efficiency and quality of information exchange but also transformed the way we interact with each other. This essay will explore several key applications of AI in communications, highlighting their benefits and the potential they hold for the future.1. Personalized CommunicationOne of the most prominent applications of AI in communications is personalization. By analyzing user data, AI algorithms can tailor communication content and channels to individual preferences. For instance, in marketing, AI-powered tools study customers' browsing history, purchase behavior, and even social media interactions to deliver personalized ads and promotions. Similarly, in customer service, chatbots use AI to understand customer queries and provide tailored responses, creating a more personalized and efficient service experience.2. Natural Language Processing (NLP)NLP is a branch of AI that enables machines to understand, interpret, and generate human language. This technology has significantly improved communication interfaces, making them more intuitive and user-friendly. Virtual assistants like Siri, Alexa, and Google Assistant utilize NLP to understand voice commands and carry out tasks accordingly. Additionally, NLP enables real-time language translation services, breaking down barriers between speakers of different languages and fostering global communication.3. Predictive Analytics and ForecastingAI's predictive capabilities have transformed communication strategies. By analyzing historical data and current trends, AI can predict future communication needs and behaviors. This is particularly useful in network management, where AI algorithms can anticipate traffic spikes and adjust resources accordingly to ensure seamless connectivity. In marketing, predictive analytics helps businesses identify potential customers and tailor communication campaigns to maximize engagement and conversions.4. Fraud Detection and CybersecurityAs cyber threats become more sophisticated, AI plays a vital role in protecting communication channels. AI-powered systems can analyze vast amounts of data in real-time, identifying patterns and anomalies that might indicate fraud orsecurity breaches. This enables prompt intervention, minimizing the impact of cyberattacks and safeguarding sensitive information.5. Enhanced Collaboration and Remote WorkIn the era of remote work, AI has facilitated seamless collaboration among teams dispersed across different locations. AI-driven communication platforms offer advanced features like real-time translation, automatic transcription, and sentiment analysis, making it easier for team members to understand and communicate with each other effectively. Furthermore, AI-powered project management tools optimize task allocation, monitor progress, and provide insights to improve collaboration efficiency.ConclusionIn conclusion, AI's applications in communications are diverse and transformative. From personalizing communication experiences to enhancing cybersecurity and facilitating remote collaboration, AI is reshaping the way we interact with each other and access information. As technology continues to evolve, the potential for AI in communications is limitless, promising even more innovative solutions that will make our lives more connected, efficient, and secure.。

ai在通信中的应用英文作文

ai在通信中的应用英文作文下载温馨提示:该文档是我店铺精心编制而成，希望大家下载以后，能够帮助大家解决实际的问题。

文档下载后可定制随意修改，请根据实际需要进行相应的调整和使用，谢谢!并且，本店铺为大家提供各种各样类型的实用资料，如教育随笔、日记赏析、句子摘抄、古诗大全、经典美文、话题作文、工作总结、词语解析、文案摘录、其他资料等等，如想了解不同资料格式和写法，敬请关注!Download tips: This document is carefully compiled by theeditor. I hope that after you download them,they can help yousolve practical problems. The document can be customized andmodified after downloading,please adjust and use it according toactual needs, thank you!In addition, our shop provides you with various types ofpractical materials,such as educational essays, diaryappreciation,sentence excerpts,ancient poems,classic articles,topic composition,work summary,word parsing,copy excerpts,other materials and so on,want to know different data formats andwriting methods,please pay attention!AI is really useful in communication. It can help us translate languages easily. For example, when we talk to someone who speaks adifferent language, AI can translate what we say in real time. That's so cool!Another thing is that AI can make communication more efficient. It can understand our intentions and answer our questions quickly. It's like having a smart assistant all the time.Also, AI can be used in video calls. It can add fun filters and effects, making the calls more interesting. And it can even recognize our faces and expressions.In short, AI has brought a lot of changes to communication. It makes our lives more convenient and fun.。

theproblemofcommunication英语作文

theproblemofcommunication英语作文With the development of our society, the connection between people becomes inevitable. People need to communicate with others as long as they are in the world. As a result, the interpersonal relationship becomes more and more important.First of all, a good interpersonal relation promote people become successful. Through the human history, we can summarize that no one can do everything well and become successful alone. Most of the great men have a good relationship among people. For many people, interpersonal relationship and their ability are equal important to their success. Keeping a good relationship with others, people can easily get assistance from others when facing difficulties. In other words, excellent interpersonal communication skills can help remove obstacles on the way to success. Secondly, a good interpersonal relation can help people live a happy life. Living in a harmonious environment need people keep a good relationship with the people around. The friendly people will form a happy atmosphere. That will make people work efficient and keep a good mood in the daily life. It is certain that people will receive happiness in the end.To sum up, as interpersonal relationship is so essential to people’s success and happiness, I advocate people get along well with others. The first thing is smile more every day.。

Epic Systems Corporation

Predictive Analysis of a Wavefront Application Using LogGP*David Sundaram-Stukel Epic Systems Corporation5301 Tokay BlvdMadison, WI 53711 dsundara@Mary K. Vernon University of Wisconsin-Madison Computer Sciences Dept.1210 W. Dayton StreetMadison, WI 53706-1685 vernon@ABSTRACTThis paper develops a highly accurate LogGP model of a complex wavefront application that uses MPI communication on the IBM SP/2. Key features of the model include: (1) elucidation of the principal wavefront synchronization structure, and (2) explicit high-fidelity models of the MPI-send and MPI-receive primitives. The MPI-send/receive models are used to derive L, o, and G from simple two-node micro-benchmarks. Other model parameters are obtained by measuring small application problem sizes on four SP nodes. Results show that the LogGP model predicts, in seconds and with a high degree of accuracy, measured application execution time for large problems running on 128 nodes. Detailed performance projections are provided for very large future processor configurations that are expected to be available to the application developers. These results indicate that scaling beyond one or two thousand nodes yields greatly diminished improvements in execution time, and that synchronization delays are a principal factor limiting the scalability of the application. KeywordsParallel algorithms, parallel application performance, LogP model, particle transport applications.1.INTRODUCTIONThis paper investigates the use of the parallel machine model called LogGP to analyze the performance of a large, complex application on a state-of-the-art commercial parallel platform. The application, known as Sweep3D, is of interest because it is a three-dimensional particle transport problem that has been identified as an ASCI benchmark for evaluating high performance parallel architectures. The application is also of interest because it has a fairly complex synchronization structure. This synchronization structure must be captured in the analytic model in order for the model to accurately predict application execution times and thus provide accurate performance projections for larger systems, new architectures, or modifications to the application. One question addressed in this research is which of variants of the LogP model [4] is best suited for analyzing the performance of Sweep3D on the IBM SP system. Since this version of Sweep3D uses the MPI communication primitives, the LogGP model [2] which includes an additional parameter, G, to accurately model communication cost for large pipelined messages, turned out to provide the requisite accuracy. Possibly due to the blocking nature of the MPI primitives, the contention at message processing resources is negligible and thus recent extensions to LogP for capturing the impact of contention [7,12] are not needed.In previous work [4,6,7], the LogP models have been applied to important but fairly simple kernel algorithms, such as FFT, LU decomposition, sorting algorithms, or sparse matrix multiply. Two experimental studies have applied the model to complex full applications such as the Splash benchmarks [9, 11]. However, in these studies, the effects of synchronization on application performance and scalability were measured empirically rather than estimated by the model. Many other previous analytic models for analyzing application performance are restricted to simpler synchronization structures than Sweep3D (e.g., [8]). One exception is the deterministic task graph analysis model [1], which has been shown to accurately predict the performance of applications with complex synchronization structures. The LogGP model represents synchronization structures more abstractly than a task graph. A key question addressed in this research is whether the more abstract representation is sufficient for analyzing a full, complex application such as Sweep3D.We construct a LogGP model that not only captures the synchronization structure but also elucidates the basic synchronization structure of Sweep3D. Similar to the approach in [2], we use communication micro-benchmarks to derive the input parameters, L, o, and G. However, as we show in section 3, deriving these parameters is somewhat more complex for MPI communication on the SP/2 than for the Meiko CS-2; thus explicit models of the MPI-send and MPI-receive primitives are developed. Although the LogGP input parameters are derived from four-processor runs of Sweep3d, the LogGP model projects performance quite accurately up to 128 processors, for several fixed total problem sizes and several cases of fixed problem size per processor. The model also quickly and easily projects performance for the very large future processor configurations that are expected to be available to the application developers.*This research is supported in part by DARPA/ITO under contract N66001-97-C-8533.Computer Sciences Technical Report #1392, University of Wisconsin-Madison, February 1999. To appear in Proc. 7th ACM SIGPLAN Symp. on Principles and Practice of Parallel Programming (PPoPP ’99), Atlanta, GA, May 1999.We show several interesting results that can be derived from the analysis.Section 2 provides a brief overview of the Sweep3D application.Section 3 derives the models of MPI-send and MPI-receive and the parameter values that characterize communication cost.Section 4 presents the LogGP equations for Sweep3D, as well as the modifications that are needed when the application utilizes the multiprocessor SMP nodes of the SP/2. In the latter case, there are two types of communication costs: intra-cluster and inter-cluster. Section 5 provides model validation results as well as performance projections for future systems. Section 6 provides the conclusions of this work.2. Sweep3DSweep3D is described in [10]. A detailed task graph showing the complex synchronization among the tasks in the version of the code that is analyzed in this paper, is given in [5]. Here we give a simple overview of this version of Sweep3D, including only the aspects that are most relevant to the LogGP model. The structure of the algorithm will be further apparent from the LogGP model presented in section 4.As its name implies, the Sweep3D transport calculations are implemented as a series of pipelined sweeps through a three dimensional grid. Let the dimensions be denoted by (i,j,k). The 3D grid is mapped onto a two-dimensional array of processors, of size m ×n , such that each processor performs the calculations for a partition in the i and j dimensions of size it ×jt ×k , as shown in Figure 1. Note that, due to the problem mapping in Figure 1, the processors in the processor grid of Figure 2 will be numbered p i,j where i varies from 1 to n and indicates the horizontal position of the processor.A single iteration consists of a series of pipelined sweeps through the 3D grid starting from each of the 8 corners (or octants) of the grid. The mapping of the sweeps to the two dimensional processor grid is illustrated in Figure 2. If mo denotes the number of angles being considered in the problem, then each processor performs it ×jt ×k ×mo calculations during the sweeps from each octant. To create a finer granularity pipeline, thus increasing parallelism in the computation, the block of data computed by a given processor is further partitioned by an angle blocking factor (mmi ) and a k-plane blocking factor (mk ). These parameters specify the number of angles and number of planes in the k-dimension, respectively,that are computed before boundary data is forwarded to the next processor in the pipeline. Each processor in the interior of the processor grid receives this boundary data from each of two neighbor processors, computes over a block based on these values, and then sends the results of its calculations to twoneighbor destination processors, determined by the direction of the sweep.In the optimized version of Sweep3D that we analyze, once all blocks at a given processor are calculated for the sweeps from a given pair of octants, the processor is free to start calculating blocks for sweeps from the next pair of octants. For example, the lower left corner processor can start to compute the first block of the sweep for octant 7 after it has computed its last block of the sweep originating from octant 6. This will be shown in greater detail in the LogGP model of Sweep3D in section 4.The pipelined sweep for octant 8 completes one iteration of the algorithm for one energy group. In the code we analyze, twelve iterations are executed for one time step. The target problems of interest to the ASCI program involve on the order of 30 energy groups and 10,000 time steps, for grid sizes on the order of 109(1000×1000×1000) or twenty million (e.g., 280×280×255). We can scale the model projections to these problem sizes, as shown in section 5.3. Communication Parameters: L, o, GBefore we present the LogGP model of Sweep3D for the SP/2, we derive models of the MPI-send and MPI-receive communication primitives that are used in the application. The MPI-send/receive models are needed in the LogGP model of Sweep3D, and are also needed to derive two of the communication parameters values,namely the network Latency (L ), and the processing overhead (o )to send or receive a message. The communication structure of Sweep3D is such that we can ignore the gap (g ) parameter, as the time between consecutive message transmissions is greater than the minimum allowed value of inter-message transmission time.Below we give the roundtrip communication times for MPI communication on the IBM SP, which are measured using simple communication micro-benchmarks. The value of G (Gap per byte )is derived directly from these measurements. We then discuss how we modeled the SP/2 MPI-send and MPI-receive primitives using the L, o, and G parameters, followed by a description of how the values of L and o are derived. A significant result is that we derive the same values of L and G (but different values of o )from the Fortran and the C micro-benchmark measurements. This greatly increases our confidence in the validity of the MPI communication models.Figure 1: Partitioning the 3D Grid in the i and j Dimensions Figure 2: The Sweeps for each OctantkNSEW(Processor Grid)3.1 Measured Communication TimesThe roundtrip communication time as a function of message sizefor a simple Fortran communication micro-benchmark is given in Figures 3 (a) and (b). For each data point, a message of the given size is sent from processor A to processor B, received by a process on processor B, and immediately sent back to A. The roundtrip time is measured on A by subtracting the time just before it calls MPI-send from the time that its MPI-receive operation completes.Each figure also includes the results of our model of the roundtrip communication, which is used to derive the L and o parameters,as discussed below.As can be seen in the figures, the measured communication time increases significantly with message size. Hence, the G parameter is required to accurately model communication cost. Two further points are worth noting from the Figure:•The communication cost changes abruptly at message size equal to 4KB, due to a handshake mechanism that is implemented for messages larger than 4KB. The handshake is modeled below.•The slope of the curve (G ) changes at message size equal to 1KB.The message processing overhead (o ) is also different for messages larger than 1 KB than for messages smaller than 1KB,due to the maximum IP packet size. Thus, we will derive separate values of G s / G l and of o s / o l for "small" (<1KB) and "large"(>1KB) messages.3.2 Models of MPI-send and MPI-receiveThe models developed here reflect a fairly detailed understanding of how the MPI-send and MPI-receive primitives are implemented on the SP/2, which we were able to obtain from the author of the MPI software. It might be necessary to modify the models for future versions of the MPI library, or if Sweep3D is run on a different message-passing architecture or is modified to use non-blocking MPI primitives. The models below illustrate a general approach for capturing the impact of such system modifications.Since the SP/2 system uses polling to receive messages, we can assume the overhead to send a message is approximately the same as the overhead to receive a message, o.For messages smaller than 4KB, no handshake is required, and the total end-to-end cost of sending and receiving a message is modeled simply as:Total_Comm = o + (message_size × G) + L + o (1)where the values of G and o depend on whether the message size is larger or smaller than 1KB.For messages larger than 4KB, the end-to-end communication requires a "handshake" in which just the header is initially sent to the destination processor and the destination processor must reply with a short acknowledgment when the corresponding receive has been posted. If the receive has been posted when the header message is sent, the end-to-end cost is modeled as follows:Total_Comm = o s + L + o s + o s + L + o l+ (message_size × G l ) + L + o l (2)Note that the processing overhead for receiving the ack is modeled as being subsumed in the processing overhead for sending the data. If the corresponding receive has not yet been posted, a additional synchronization delay will be incurred. This delay is modeled in the next section.In addition to the total cost for communication given above, the LogGP model for Sweep3D requires separate costs for sending and receiving messages. For message size less than 4KB:Send = o (3a) Receive = o (3b)where the value of o depends on the message size. For message size greater than or equal to 4KB:Send = o s + L + o s + o s + L + o l (4a)Receive = o s + L + o l + (message_size × G l ) + L + o l (4b)The receive cost includes the time to inform the sending processor that the receive is posted, and then the delay for the message to arrive.3.3 Communication Parameter ValuesUsing the above equations for Total_Comm and the measured round-trip communication times, we can derive the values of L,o s , o l , G s , and G l , which are given in Table 1. The values of G s and G l are computed directly from the slope of the curve (in Figure 3)for the respective range of message sizes. To derive L and o , we solve three equations for Total_Comm (for message sizes less than 1KB, between 1-4KB, and greater than 4KB, respectively) in three unknowns (L , o s , and o l ). Applying this method to the roundtrip time measurements obtained with C micro-benchmarks yields the same values of L and G as for the measurementsFigure 3: MPI Round Trip Communication Times.1101001000100001000001E+06Message SizeT i m e (u s e c )obtained with Fortran benchmarks, although the value of o is different, as shown in Table 1. This greatly increases our confidence in the validity of the above models of the MPI communication primitives. Using the parameter values derived in this way, the measured and modeled communication costs differ by less than 4% for messages between 64-256KB, as shown in Figure 3. Note that although the measured and modeled values seem to diverge at message size equal to 8KB in figure 2(a),figure 2(b) shows that the values for message sizes above 8KB are in good agreement.4. The LogGP Model of Sweep3DIn this section we develop the LogGP model of Sweep3D, using the models of the MPI communication costs developed in section 3. We first present the model that assumes each processor in the m ×n processor grid is mapped to a different SMP node in the SP/2. In this case, network latency is the same for all communication. We then give the modified equations for the case that 2×2 regions of the processor grid are mapped to a single (four-processor) SMP node in the SP/2. The round-trip times and parameter values computed in section 3 were for communication between processors in different SMP nodes. The same equations can be used to compute intra-node communication parameters.4.1 The Basic ModelThe LogGP model takes advantage of the symmetry in the sweeps that are performed during the execution, and thus calculates the estimated execution time for sweeps from one octant pair and then uses this execution time to obtain the total execution time for all sweeps, as explained below.During a sweep, as described in section 2, a processor waits for input from up to two neighbor processors and computes the values for a portion of its grid of size mmi × mk × it × jt. The processor then sends the boundary values to up to two neighbor processors,and waits to receive new input again. Using costs associated with each of these activities, we develop the LogGP model summarized in Table 2, which directly expresses the precedence andsend/receive synchronization constraints in the implemented algorithm.The time to compute one block of data is modeled in equation (5)of Table 2. In this equation, W g is the measured time to compute one grid point, and mmi , mk , it and jt are the input parameters,defined in section 2, that specify the number of angles and grid points per block per processor.Consider the octant pair (5,6) for which the sweeps begin at the processor in the upper-left corner of the processor grid, as shown in Figure 2. Recall that the upper-left processor is numbered p 1,1.To account for the pipelining of the wavefronts in the sweeps, we use the recursive formula in equation (6) of Table 2 to compute the time that processor p i,j begins its calculations for these sweeps,where i denotes the horizontal position of the processor in the grid. The first term in equation (6) corresponds to the case where the message from the West is the last to arrive at processor p i,j . In this case, the message from the North has already been sent but cannot be received until the message from the West is processed due to the blocking nature of MPI communications. The second term in equation (6) models the case where the message from the North is the last to arrive. Note that StartP 1,1 = 0, and that the appropriate one of the two terms in equation (6) is deleted for each of the other processors at the east or north edges of the processor grid.The Sweep3D application makes sweeps across the processors in the same direction for each octant pair. The critical path time for the two right-downward sweeps is computed in equation (7) of Table 2. This is the time until the lower-left corner processor p 1,m has finished communicating the results from its last block of the sweep for octant 6. At this point, the sweeps for octants 7 and 8(to the upper right) can start at processor p 1,m and proceed toward p n,1. Note that the subscripts on the Send and Receive terms in equation (7) are included only to indicate the direction of the communication event, to make it easier to understand why the term is included in the equation. The send and receive costs are as derived in section 3.2.The critical path for the sweeps for octants 7 and 8 is the time until all processors in the grid complete their calculations for the sweeps, since the sweeps from octants 1 and 2 (in the next iteration) won’t begin until processor p n,1 is finished. Due to the symmetry in the Sweep3D algorithm, mentioned above, the time for the sweeps to the Northeast is the same as the total time for the sweeps for octants 5 and 6, which start at processor p 0,0 and move Southeast to processor p n,m . Thus, we compute the critical path time for octants 7 and 8 as shown in equation (8) of Table 2.Equation (8) represents the time until processor p n,m has finished its last calculation for the second octant pair. The processorW i,j = W g × mmi × mk × it × jt (5) StartP i,j = max (StartP i –1,j + W i −1,j + Total_Comm + Receive, StartP i,j −1 + W i,j −1 + Send + Total_Comm) (6) T 5,6 = startP 1,m + 2[(W 1,m + Send E + Receive N + (m-1)L) × #k-blocks × #angle-groups] (7) T 7,8 = startP n-1,m + 2[(W n-1,m + Send E + Receive W + Receive N + (m–1)L+ (n-2)L) × #k-blocks × #angle-groups]+ Receive W + W n,m (8) T = 2 ( T 5,6 + T 7,8 ) (9)Table 2 LogGP Model of Sweep3DMessage Size:≤ 1024> 1024L 23 µsec 23 µsec o (Fortran)23 µsec 47 µsec o (C)16 µsec 36 µsec G0.07 µsec0.03 µsecTable 1. SP/2 MPI Communication Parametersdirectly to its East, p n-1,m, must start computing, calculate and communicate all needed results from the blocks for both octants, and then wait for processor p n,m to receive the results from the last block of these calculations and compute the results based on this block.Due to the symmetry between the sweeps for octants 1 through 4 and the sweeps for octants 5 through 8, the total execution time of one iteration is computed as in equation (9) of Table 2.The equation for T5,6 contains one term [(m–1)L], and the equation for T7,8contains two terms [(m–1)L and(n-2)L], that account for synchronization costs. These synchronization terms are motivated by the observation that measured communication times within Sweep3D are greater than the measured MPI communication cost discussed in section 3. The (m-1)L term in T5,6 and T7,8 captures the delay caused by a send which is blocked until the destination processor posts the corresponding receive. This delay accumulates in the j direction; thus the total delay at p1,m depends on the number of processors to its North (m-1). Furthermore, this synchronization cost is zero for the problems with message sizes smaller than 4KB, since in this case, the processor sends the message whether or not the corresponding receive has been posted. The second synchronization delay in T7,8, (n-2)L, represents difference between when a receive is posted, and when a message is actually received from the sending processor. Since a processor receives from the North after the West on a southeast sweep, it is more likely to wait for the message from the West. Since this delay is cumulative over all processors in the i dimension, at processor p(n-1),m we model this delay as (n-2)L. Notice that this receive synchronization term is 0 for processors on the west edge of the processor grid since there are no processors to its West from which to receive a message. This is why it was not included in the T5,6 expression above.4.2The Model for the Clustered SMP NodesA few modifications to the above model are needed if each 2×2 region of processor grid is mapped to a single four-processor SMP cluster in the IBM SP/2, rather than mapping each processor in the grid to a separate SMP node. These changes are outlined here, in anticipation of the next generation of MPI software for the SP that will support full use of the cluster processors.Let L local denote the network latency for an intracluster message, L remote denote the latency for an intercluster message, and L avg= (L local+ L remote)/2. In the following discussion, o and G are assumed to be the same for intra-cluster and inter-cluster messages, but the equations can easily be modified if this is not the case. Let L and R be subscripts that denote a model variable (e.g,. TotalComm, Send, or Receive) that is computed using L local or L remote, respectively. Using this notation, the modified equations that compute the execution time of Sweep3D are given in Table 3 and described below.Recall that processor numbering starts from 1 in both the i and j dimensions. Also recall that, for processor p i,j, i denotes its horizonal position in the processor grid. If both i and j are even, then all incoming messages are intra-cluster and all outgoing messages are inter-cluster. The vice versa is true if both i and j are odd. This means that StartP i,j is computed with TotalComm L, Receive L, and Send L(for the incoming messages) in the former case, and with TotalComm R, Receive R, and Send R in the latter case. For i odd and j even, the variables in the first term of StartP i,j are for inter-cluster communication and the communication variables in the second term are for intra-cluster communication. The vice versa is true for i even and j odd.The Send and Receive variables in the equations for T5,6 and T7,8 are all intra-cluster variables, assuming that the number of processors in each of the i and j dimensions is even when mapping 2×2 processor regions to the SMP clusters. The synchronization terms in T5,6and T7,8are computed using L avg. These are the only changes required in the model.The modified model has been validated against detailed simulation [3]. However, since we cannot yet validate them with system measurements (because efficient MPI software for intra-cluster communication doesn’t yet exist), only results for the case that each processor is mapped to a separate SMP node are given in this paper. Nevertheless, the changes to the model for full cluster use are simple and illustrate the model’s versatility. Furthermore, these equations can be used to project system performance for the next generation MPI software.4.3Measuring the Work (W)The value of the work per grid point, W g, is obtained by measuring this value on a 2x2 grid of processors. In fact, to obtain the accuracy of the results in this paper, we measured W g for each per-processor grid size, to account for differences (up to 20%) that arise from cache miss and other effects. Since the Sweep3D program contains extra calculations (“fixups”) for five of the twelve iterations, we measure W g values for both of these iteration types. Although this is more detailed than the creators of LogP/LogGP may have intended, the increased accuracy is substantial and needed for the large scale projections in section 5. Furthermore, our recursive model of Sweep3D only represents thei even, j even: StartP i,j = max (StartP i –1,j + W i−1,j + Total_Comm L + Receive L , StartP i,j−1 + W i,j−1 + Send L + Total_Comm L) i odd, j odd: StartP i,j = max (StartP i –1,j + W i−1,j + Total_Comm R + Receive R , StartP i,j−1 + W i,j−1 + Send R + Total_Comm R)i odd, j even: StartP i,j = max (StartP i –1,j + W i−1,j + Total_Comm R + Receive R , StartP i,j−1 + W i,j−1 + Send L + Total_Comm L) i even, j odd: StartP i,j = max (StartP i –1,j + W i−1,j + Total_Comm L + Receive L , StartP i,j−1 + W i,j−1 + Send R + Total_Comm R)T5,6 = startP1,m+ 2[(W1,m+ Send E+ Receive N + (m-1)L avg) × #k-blocks × #angle-groups]T7,8 = startP n-1,m+ 2[(W n-1,m+Send E+Receive W+ Receive N+ (m–1)L avg+ (n-2)L avg) × #k-blocks × #angle-groups]+ Receive W + W n,m Table 3: Modified LogGP Equations for Intra-Cluster Communication on the SP/2sweeps of the Sweep3D code. In addition, we measure the computation time before and after this main body of the code (i.e.,between the iterations for a time step). These computation times,denoted W before and W after , are measured during a single processor run of a specific problem size. All model parameters are thus measured using simple code instrumentation and relatively short one, two, and four-processor runs. In the next section we investigate how accurately the model predicts measured execution time for the Sweep3D application.5. Experimental ResultsIn this section we present the results obtained from the LogGP model. We validate the LogGP projections of Sweep3D running time against measured running time for up to 128 processors and then use the LogGP model to predict and evaluate the scalability of Sweep3D to thousands of processors, for two different problem sizes of interest to the application developers. Unless otherwise stated the reported execution times are for one energy group and one time step with twelve iterations in the time step.In Figure 4 we compare the execution time predicted by the LogGP model to the measured execution time for the Fortranversion of Sweep3d on up to 128 SP/2 processors, for fixed total problems sizes (150×150×150 and 50×50×50), and k-blocking factor, mk , equal to 10. As the number of processors increases, the message size and the computation time per processor decrease,while the overhead for synchronization increases. For these problem sizes and processor configurations, the message sizes vary from over 16KB to under 1KB; there is remarkably high agreement between the model estimates and the measured system performance across the entire range. Figure 5 shows that the larger problem size achieves reasonably good speedup (i.e., low communication and synchronization overhead) on 128 processors while the smaller problem size does not. Note that the model is highly accurate for both cases.In Figure 6, we show the predicted and measured application execution time as a function of the number of processors on the SP/2, for two different cases of fixed problem size per processor .In Figure 6(a) each processor has a partition of the three-dimensional grid that is of size 20×20×1000. In Figure 6(b), each processor has a partition of size 45x45x1000. In these experiments, the total problem size increases as the number of processors increases. The agreement between the model estimates(a) Problem size: 150×150×150 (b) Problem size: 50×50×50Figure 4: Validation of the LogGP Model for Fixed Total Problem Size(Fortran Code, mk=10, mmi=3)(a) Up to 128 processors(b) Up to 2500 processorsFigure 5: Sweep3D Speedups for Fixed Total Problem Sizes in Figure 4(Fortran Code, mk=10, mmi=3)020*********120140050100150ProcessorsS p e e d u p0501001502002503003504004500100020003000ProcessorsS p e e d u p。

造纸术英语作文

造纸术英语作文Paper is an essential part of our daily lives, used for writing, printing, packaging, and a myriad of other applications. However, the process of creating this versatile material has a rich history that dates back to ancient China. In this essay, we will explore the origins of papermaking,its evolution over time, and its impact on the world.The birth of paper is attributed to a Chinese court official named Cai Lun in the 2nd century AD. He is said to have invented paper by using mulberry bark, hemp, and other plant fibers, which he mashed and pressed into sheets. This innovation was a significant departure from the use of silk and bamboo, which were the primary writing materials at the time. The invention of paper revolutionized the way information was recorded and disseminated, leading to a cultural and intellectual renaissance in China.As the centuries passed, the art of papermaking spread across Asia, reaching the Islamic world by the 8th century and then Europe by the 13th century. Each region adapted the process to suit local materials and preferences. In Japan, for example, the use of washi paper, made from the fibers of the gampi tree, became prevalent. The Europeans, on the other hand, experimented with linen and cotton fibers, leading to the development of the strong, durable paper we associate with books and documents today.The industrial revolution brought about significant changesto the papermaking process. In 1803, Nicolas-Louis Robert invented a machine that could produce a continuous sheet of paper, which greatly increased the efficiency and output of paper production. This mechanization paved the way for the mass production of paper, making it more affordable and accessible to the general public.In the modern era, papermaking has become a highly sophisticated industry that incorporates advanced technologyto produce a wide variety of paper types. From the glossy pages of magazines to the absorbent tissues we use daily,each type of paper is crafted with a specific purpose in mind. The process now involves pulping, where fibers are separated from water and other impurities, followed by the formation of the paper sheet through a mesh, pressing to remove excess water, and drying.However, with the advent of digital technology, there hasbeen a shift towards electronic communication and a declinein the demand for paper. This has led to concerns about the environmental impact of paper production, including deforestation and water pollution. As a result, there is a growing emphasis on sustainable practices, such as using recycled materials and developing new, eco-friendly methodsof papermaking.In conclusion, the art of papermaking has come a long wayfrom its humble beginnings in ancient China. It has played a crucial role in the development of human civilization,enabling the preservation and sharing of knowledge andculture. As we continue to innovate and adapt to new technologies, it is essential to remember the importance of sustainability and the preservation of our natural resources for future generations. Paper, while seemingly simple, is a testament to human ingenuity and our ability to shape the world around us.。

mpi的介绍

MPI使用C語言學校：台北科技大學編寫者：呂宗螢指導教授：梁文耀老師MPI簡介MPI全名Message Passing Interface，MPI is a language-independent communications protocol used to program parallel computers定義了process和process之間傳送訊息的一個標準，適用於各種不同的傳遞媒介。

不止是單一電腦內的process和process傳送訊息，還可以在網路上不同電腦間的process與process的溝通。

目的是希望能提供一套可移值性和高效率的傳送訊息的標準。

有相同功能的是PVM (Parallel Virtual Machine)，但現在一般較多人使用的是MPI。

因為他只是一個標準(interface/ standard)，所以MPI本身不是library，只是提供給各個單位自行製作MPI時所必須遵循標準。

所以市面上有許多的ex：MPICH、MS MPI、Lan/MPI…等，均是遵循MPI的標準所實作出來的MPI，而各廠商亦可以針對自己的硬體做最佳化。

MPICH便是其中之一實作MPI標準的library，提供了MPI的function，以便程式設計師撰寫平行運算程式，也是最基本MPI實作，其優點是可以跨平台(只能要能裝mpich的環境均可，但程式的撰寫不能相依於OS，ex：使用pthread.h)，提供語言有C、Fortan 77、C++、Fortan 90(c++和fortan 90在mpich2才支援)。

撰寫程式和執行的步驟1.啟動MPI環境(亦可以在Compiler完再啟動，mpd.hosts定義了一些mpi環境的主機名稱)mpdboot -n 4 -f mpd.hosts (-n 是指啟動主機的數目，-f 是指定義主機的檔案)2.撰寫MPI程式vi hello.cpilempicc hello.c –o hello.o4.執行程式mpiexec –n 4 ./hello.o (-n 是指process(行程)的數目)5.結束MPImpdallexit撰寫平行程式的基本觀念1.需由程式設計師來規畫平行化程式：所謂平行程式並不是指說你寫好一個程式，只要丟到平行運算環境，該環境便會自動幫你把程式分開，然後做平行處理。

The Importance of Interpersonal Communication

The Importance of InterpersonalCommunicationInterpersonal communication is an essential aspect of human interaction that plays a significant role in our daily lives. It is the process of exchanging information, feelings, and meaning through verbal and non-verbal cues between two or more people. The importance of interpersonal communication cannot be overstated, as it is crucial for building and maintaining relationships, resolving conflicts, and achieving personal and professional success. One of the key reasons why interpersonal communication is so important is its role in building andmaintaining relationships. Whether it is with family, friends, colleagues, or romantic partners, effective communication is essential for fostering strong and healthy connections. By expressing our thoughts, feelings, and needs to others, we can develop a deeper understanding of each other and build trust and intimacy. Without effective communication, misunderstandings and conflicts can arise,leading to strained or broken relationships. Furthermore, interpersonal communication is vital for resolving conflicts and addressing issues within relationships. When disagreements or misunderstandings occur, open and honest communication is necessary to express concerns, listen to the other person's perspective, and work towards finding a resolution. Through effective communication, individuals can address underlying issues, clarify misunderstandings, and find common ground, ultimately strengthening the relationship. In addition to its role in personal relationships, interpersonal communication is also crucial for achieving success in the professional world. Whether it is in the workplace, during job interviews, or in networking situations, the ability to communicate effectively with colleagues, superiors, and clients is essential. Strong communication skills can help individuals convey their ideas, collaborate with others, and build rapport, ultimately contributing to their professional success and advancement. Moreover, interpersonal communication plays a significant role in personal development and self-expression. By effectively communicating our thoughts, feelings, and needs, we can assert ourselves, set boundaries, and express our individuality. This can lead to increased self-confidence, improved self-esteem, and a greater sense of empowerment in various aspects of our lives. Another perspective to consider is the impact of interpersonal communication on mental and emotional well-being. When individuals feel heard, understood, and supported through effective communication, it can have a positive impact on their mental health. On the other hand, a lack of communication or poor communication skills can lead to feelings of isolation, frustration, and stress, which can take a toll on one's overall well-being. Furthermore, interpersonal communication is essential for navigating social interactions and understanding the perspectives and experiences of others. Through effective communication, individuals can develop empathy, cultural sensitivity, and a greater appreciation for diversity. This, in turn, can lead to stronger and more inclusive communities and contribute to the overall well-being of society. In conclusion, interpersonal communication is a fundamental aspect of human interaction that plays a crucial role in various aspects of our lives. Whether it is in building and maintaining relationships, resolving conflicts, achieving professional success, personal development, or promoting mental and emotionalwell-being, effective communication is essential. By honing our communication skills and prioritizing open and honest dialogue, we can foster stronger connections, navigate challenges, and ultimately lead more fulfilling lives.。

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Abstract | The statistical analysis of traces taken from the NAS Parallel Benchmarks can tell one much about the type of network tra c that can be expected from scienti c applications run on distributed-memory parallel computers. For instance, such applications utilize a relatively few number of communication library functions, the length of their messages is widely varying, they use many more short messages than long ones, and within a single application the messages tend to follow relatively simple patterns. Information such as this can be used by hardware and software designers to optimize their systems for the highest possible p Use of the MPI Communication Library in the NAS Parallel Benchmarks
Theodore B. Tabe,
Member, IEEE Computer Society, and Quentin F. Stout, Senior Member, IEEE Computer Society
NPB 3 . The rest of this paper describes in further detail the NPB and the results obtained from analyzing the frequency and type of message calls which occur within the NPB. Section II of the paper describes the NPB. Section III describes the instrumentation methodology used on the NPB. Following that is Section IV, which describes the assumptions made about the manner in which the MPI messagepassing library was implemented. Section V gives a sumKeywords | benchmarks, trace analysis, message-passing, mary of the data gathered from the traces. Section VI prodistributed memory parallel computer, parallel computing vides an explanation for the patterns observed, in terms of the nature of the communication patterns of the NPB. I. Introduction Section VII provides some nal conclusions. Parallel computing is a computer paradigm where multiple processors attempt to co-operate in the completion of a II. NAS Parallel Benchmarks Description single task. Within the parallel computing paradigm, there are two memory models: shared-memory and distributed memory. The shared-memory model distinguishes itself The NAS Parallel Benchmarks are a set of scienti c by presenting the programmer with the illusion of a sin- benchmarks issued by the Numerical Aerodynamic Simulagle memory space. The distributed-memory model, on the tion NAS program located at the NASA Ames Research other hand, presents the programmer with a separate mem- Center. The benchmarks have become widely accepted as ory space for each processor. Processors, therefore, have to a reliable indicator of supercomputer performance on scishare information by sending messages to each other. To enti c applications. As such, they have been extensively send these messages, usually applications call a standard analyzed 4 , 5 , 6 . The benchmarks are largely derived communication library. The communication library is usu- from computational uid dynamics code and are currently ally MPI Message Passing Interface 1 or PVM Parallel on version 2.2. The NAS Parallel Benchmarks 2.2 includes Virtual Machine 2 , with MPI rapidly becoming the norm. implementations of 7 of the 8 benchmarks in the NAS ParAn important component in the performance of a allel Benchmarks 1.0 suite. The eighth benchmark shall be distributed-memory parallel computing application is the implemented in a later version of the NAS Parallel Benchperformance of the communication library the application marks. The benchmarks implemented are: uses. Therefore, the hardware and software systems providing these communication functions must be tuned to the BT: a block tridiagonal matrix solver. highest degree possible. An important class of information EP: Embarrassingly Parallel, an application where there that would aid in the tuning of a communication library is very minimal communication amongst the processes is an understanding of the communication patterns that FT: a 3-D FFT PDE solver benchmark. occur within applications. This includes information such IS: integer sort as the relative frequency with which the various functions LU: an LU solver. within the communication library are called, the lengths of MG: a multigrid benchmark. the messages involved, and the ordering of the messages. SP: a pentadiagonal matrix solver. Since it is not realistic to examine all the distributedThe benchmark codes are written in Fortran with MPI memory parallel applications in existence, one looks to nd function calls, except for the IS benchmark which is writa small set of applications that reasonably represents the ten in C with MPI function calls. The NAS Parallel Benchentire eld. The representative set of applications that marks can be compiled into three problem sizes known as was chosen was the widely-used NAS Parallel Benchmarks classes A, B, and C. The class A benchmarks are tailored to Theodore B. Tabe is with the Advanced Computer Architecture run on moderately powerful workstations. Class B benchLaboratory, University of Michigan. Email: tabe@. marks are meant to run on high-end workstations or small Quentin F. Stout is with the Electrical Engineering and Com- parallel systems. Class C benchmarks are meant for highputer Science Department, University of Michigan. Email: qstout@. end supercomputing.