曙光携NVIDIA重磅发布GPU服务器新品
浪潮-曙光刀片服务器型号详细介绍
RAID 1E
支持Intel Xeon
Intel 5500系列
5500/5600系列处理器; 高端芯片组
最多2个，可选1个
提供10个内存插槽，最大 80GB DDR III 800/1066 /1333 ECC内存
提供两个2.5”热插拔 SAS/SATA磁盘槽位，可 选Raid功能，支持 Raid0，1
双千兆以太网控制器 集成图形控制器
板载BMC管理芯片
每个CPU Blade都预留 两个高速PCI-E扩展链 路，配合刀片机箱后侧 高速网络模块和I/O模 块的扩展
双千兆以太网控制器 集成图形控制器
板载BMC管理子卡
管理模块
配套介质
光学通路状态指示OPSI
外部存储器
标配一个，可选1+1冗
余。可实现远程虚拟介
曙光CB60-G2
CB60-G2符合开放性标准，具有强大灵
活的网络和IO扩展能力，多样的磁盘
刀片式
配置方案，全面便捷的系统管理，强 大的功能计算能力，按需配置、可伸
缩，高效智能的电源策略和散热策
略，并且具有绿色节能。
曙光CB60-G
有效的降低了高性能计算中心、数据 中心对于空间的要求；大幅增加了计 算密集性，与传统解决方案相比同样 的机架空间可多提供42.8%的计算处理 刀片式 能力；通过对各功能模块的整合，显 著减少连接线缆。BladeEngine刀片机 箱，高可用机箱中板；使用自动智能 调节策略的电源模块SRPM；共享USB设 备实现Share Media功能
本地STA DOM硬盘位，支持16G,支持
RAID 0,1 提供2个IO扩展接口
标准刀片机箱、IO刀片、高可用中板
、Infiniband模块、计算刀片、以太
H3C工业标准服务器产品介绍
高性能机架
4000系列
6000系列
大中型企业
大型企业
文件和打印 信息处理 基础架构应用
云计算 大数据 分布式计算
云计算和大数据 虚拟化和分布式计算
数据库 U2L 关键应用
应用优化 4300/5200
专业应用
异构计算 温冷存储
T1100 G3
R2900 G3 R2700 G3
R4900 G3 R4700 G3
• 可信计算 • 安全启动 • 防病毒与防攻击
H3C G3服务器打造国际品质
H3C UniServer R4900 G3
HPE ProLiant DL380 Gen10
卓越的设计 极致免工具 精良的用料
H3C G3服务器打造国际品质
品质控制贯穿始终
防御百年一遇的地震危害
系统 保证
选择 验证
开发 验证
占比25%
刀片服务器中国市场按行业的分布
2017年全年的行业分布
Vendor 建筑 分销 教育 金融 政府 医疗 互联网 制造 媒体 能源 服务 运营商 运输 公共事业
Huawei
NO.1
H3C NO.1 NO.1 NO.1 NO.1 NO.1 NO.1
NO.1
Sugon
NO.1
面向现代数据中心的高扩展性通用机架服务器
H3C UniServer R4900 G3服务器
大型数据中心横向扩展应用
01
虚拟化、云计算、大数据
大中型数据中心的数据库应用 02
ERP、CRM、数据仓库
大规模计算 03
高性能计算、深度学习、人工智能
中小型数据中心的虚拟化整合 04
将多个工作负载整合到单一物理机上
RTX 2060 SUPER加持，体积不到6L的迷你工作站剪辑渲染全HOLD住！
RTX 2060 SUPER加持，体积不到6L的迷你工作站剪辑渲染全HOLD住！作者：来源：《电脑报》2020年第14期随着5G网络的兴起，视频社交应用越来越普及，同时更多的用户也加入到了内容创意设计工作者行列中来，而这就对数字内容生产力工具的性能提出了更高的要求，因此高性能设计师PC的市场需求变得越来越旺盛。
而GPU图形技术领军者NVIDIA则利用GPU计算能力远超CPU的优势打造了专业设计性能更加强悍的NVIDIA STUDIO与RTX STUDIO解决方案，作为NVIDIA主力合作伙伴的索泰也在此方案基础上推出了Inspire Studio系列迷你设计师PC，为专业设计师们提供了高效而易用的选择。
索泰旗下的ZBOX系列迷你电脑产品在玩家中的口碑相当不错，而这一次推出的Inspire Studio则更加有特色。
和之前偏重极致小巧而采用移动平台配件的ZBOX mini系列不同，定位高效生产力工具的Inspire Studio更注重强悍的性能，因此配备了桌面级的8核心处理器与RTX 2060 SUPER显卡（性能达到高配台式机的水平，远远强于性能级笔记本），同时也拥有更为强大的扩展性。
不过，就算塞进了这么多高端配件，Inspire Studio仍然将体积控制在了6L以内，远低于一般台式机的30～40L，依然称得上是相当迷你。
正是因为有了这样小巧的身材，让索泰Inspire Studio非常适合需要性能比设计师笔记本强（达到高配设计师PC的水平），同时又需要较好便携性的应用场景，或者是喜欢整洁桌面的高颜控设计师用户。
例如在活动或会议现场需要实时剪辑视频，那么就可以用Inspire Studio接上大屏幕电视操作，效率远高于笔记本，用完之后装进拉杆箱即可轻松携带。
那么，搭配了桌面级RTX 2060 SUPER显卡与8核心CPU的Inspire Studio的散热做得怎么样呢？除了机身顶部全面覆盖散热孔之外，拆机之后我们还可以看到机身内部为CPU提供了大面积的嵌铜散热器（外加两个智能温控风扇主动散热），同时显卡采用了横置的安装方式，而鋁制机身外壳也被巧妙地利用起来变成了超大面积的散热片（机身两侧也各配备了一个静音风扇，一进一出形成高效散热风道），如此一来即便是满负荷渲染或者运行游戏，也能保证系统工作的稳定性和可靠性。
NVIDIA Compute Sanitizer 2023.3.1 发行说明说明书
Release NotesTABLE OF CONTENTS Chapter 1. Release Notes (1)1.1. Updates in 2023.3.1 (1)1.2. Updates in 2023.3 (1)1.3. Updates in 2023.2.1 (2)1.4. Updates in 2023.2 (2)1.5. Updates in 2023.1.1 (2)1.6. Updates in 2023.1 (3)1.7. Updates in 2022.4.1 (3)1.8. Updates in 2022.4 (3)1.9. Updates in 2022.3 (3)1.10. Updates in 2022.2.1 (4)1.11. Updates in 2022.2 (4)1.12. Updates in 2022.1.1 (4)1.13. Updates in 2022.1 (4)1.14. Updates in 2021.3.1 (4)1.15. Updates in 2021.3 (5)1.16. Updates in 2021.2.3 (5)1.17. Updates in 2021.2.2 (5)1.18. Updates in 2021.2.1 (5)1.19. Updates in 2021.2 (5)1.20. Updates in 2021.1.1 (6)1.21. Updates in 2021.1 (6)1.22. Updates in 2020.3.1 (6)1.23. Updates in 2020.3 (6)1.24. Updates in 2020.2.1 (6)1.25. Updates in 2020.2 (6)1.26. Updates in 2020.1.2 (7)1.27. Updates in 2020.1.1 (7)1.28. Updates in 2020.1 (7)1.29. Updates in 2019.1 (7)Chapter 2. Known Limitations (8)Chapter 3. Known Issues (9)Chapter 4. Support (10)4.1. Platform Support (10)4.2. GPU Support (10)LIST OF TABLES T able 1 Platforms supported by Compute Sanitizer (10)1.1. Updates in 2023.3.1‣Fixed error output for WGMMA instructions.1.2. Updates in 2023.3‣Added support for Heterogeneous Memory Management (HMM) and Address Translation Service (ATS). The feature is opt-in using the --hmm-supportcommand-line option.‣Added racecheck support for device graph launches.‣Added the ability to suppress known issues using the --suppressions command-line option. See the suppressions documentation for more information.‣Added support for external memory objects. This effectively adds support for Vulkan and D3D12 interop.‣Added device backtrace support for WSL.‣Improve PC offset output. It is now printed next to the function name to clarify it is an assembly offset within that function.‣Several command-line options no longer require to explicitly specify "yes" or "no"when they are used.‣Renamed the options --kernel-regex and --kernel-regex-exclude to --kernel-name and --kernel-name-exclude.‣Added the regex filtering key to --kernel-name and --kernel-name-exclude.‣Added new command-line option --racecheck-indirect-barrier-dependency to enable indirect cuda::barrier tracking in racecheck.‣Added new command-line option --coredump-behavior to control the target application behavior after generating a GPU coredump.‣Added new command-line option --detect-missing-module-unload to detect missing calls to the cuModuleUnload driver API.‣Added new command-line option --preload-library to make the target application load a shared library before the injectiion libraries.‣Fix initcheck false positive when memory loads are widened and include padding bytes.‣Fix potential hang in racecheck and synccheck tools when the bar.arrive instruction is used.‣Added patching API support for the setsmemsize instruction.‣Added patching API support for __syncthreads() after the barrier is released.1.3. Updates in 2023.2.1‣Fixed potential racecheck hang on H100 when using thread block clusters.‣Compute Sanitizer 2023.2.1 is incorrectly versioned as 2023.2.0 and need to be differentiated by its build ID 33053471.1.4. Updates in 2023.2‣Added support for CUDA device graph launches.‣Added racecheck support for cluster entry and exit race detection for remote shared memory accesses. See the cluster entry and exit race detection documentation for more information.‣Added support for CUDA lazy loading when device heap checking is enabled.Requires CUDA driver version 535 or newer.‣Added support for tracking child processes launched with system() orposix_spawn(p) when using --target-processes all.‣Added support for st.async and red.async instructions.‣Improved support for partial warp synchronization using cooperative groups in racecheck.‣Improved support for cuda::barrier::wait() on SM 9.x.‣Added coredump support for Pascal architecture and multi-context applications.‣Added support for OptiX 8.0.‣Improved performance when using initcheck in OptiX applications in some cases.Using initcheck to track OptiX applications now requires the option --check-optix yes.1.5. Updates in 2023.1.1‣Fixed bug where memcheck would report out-of-bound accesses when loading user parameter values using a ternary operator.‣Fixed potential crash when using leakcheck with applications using CUBLAS.‣Fixed potential false positives when using synccheck or racecheck with applications using CUDA barriers.1.6. Updates in 2023.1‣Added racecheck support for distributed shared memory.‣Extended stream-ordered race detection to cudaMemcpy APIs.‣Added memcheck, synccheck and patching API support for warpgroup operations.‣Added --coredump-name CLI option to set the coredump file name.‣Added support for Unicode file paths.‣Added support for OptiX 7.7.1.7. Updates in 2022.4.1‣Fixed bug where synccheck would incorrectly report illegal instructions for code using cluster.sync() and compiled with --device-debug‣Fixed incorrect address reports in SanitizerCallbackMemcpyAsync in some specific cases, leading to potential invalid results in memcheck and racecheck.‣Fixed potential hangs and invalid results with racecheck on OptiX applications.‣Fixed potential crash or invalid results when using CUDA Lazy Module Loading with memcheck or initcheck if --check-device-heap is enabled. Lazy Module Loading will be automatically disabled in these cases.1.8. Updates in 2022.4‣Added support for __nv_aligned_device_malloc.‣Added support for ldmatrix and stmatrix instructions.‣Added support for cache control operations when using the --check-cache-control command-line option.‣Added new command-line option --unused-memory-threshold to control the threshold for unused memory reports.‣Improved support for CUDA pipeline memcpy-async related hazards in racecheck.1.9. Updates in 2022.3‣Added support for the NVIDIA GH100/SM 9.x GPU architecture.‣Added support for the NVIDIA AD10x/SM 8.9 GPU architecture.‣Added support for lazy kernel loading.‣Added memcheck support for distributed shared memory.‣Added new options --num-callers-device and --num-callers-host to control the number of callers to print in stack traces.‣Added support for OptiX 7.6 applications.‣Fix bug on Linux ppc64le where the host stack trace was incomplete.1.10. Updates in 2022.2.1‣Fixed incorrect device backtrace for applications compiled with -lineinfo.1.11. Updates in 2022.2‣Added memcheck support for use-before-alloc and use-after-free race detection. See the stream-ordered race detection documentation for more information.‣Added leakcheck support for asynchronous allocations, OptiX resources and CUDA memmap (on Linux only for the latter).‣Added option to ignore CUDA_ERROR_NOT_FOUND error codes returned by the cuGetProcAddress API.‣Added new sanitizer API functions to allocate and free page-locked host memory.‣Added sanitizer API callbacks for the event management API.1.12. Updates in 2022.1.1‣Fixed initcheck issue where the tool would incorrectly abort a CUDA kernel launch after reporting an uninitialized access on Windows with hardware schedulingenabled.1.13. Updates in 2022.1‣Added support for generating coredumps.‣Improved support for stack overflow detection.‣Added new option --target-processes-filter to filter the processes being tracked by name.‣Added initcheck support for asynchronous allocations. Requires CUDA driver version 510 or newer.‣Added initcheck support for accesses on peer devices. Requires CUDA driver version 510 or newer.‣Added support for OptiX 7 applications.‣Added support for tracking the child processes of 32-bit processes in multi-process applications on Linux and Windows x86_64.1.14. Updates in 2021.3.1‣Fixed intermittent issue on vGPU where synccheck would incorrectly detect divergent threads.‣Fixed potential hang when tracking several graph launches.1.15. Updates in 2021.3‣Improved Linux host backtrace.‣Removed requirement to call cudaDeviceReset() for accurate reporting of memory leaks and unused memory features.‣Fixed synccheck potential hang when calling __syncthreads in divergent code paths on Volta GPUs or newer.‣Added print of nearest allocation information for memcheck precise errors in global memory.‣Added warning when calling device-side malloc with an empty size.‣Added separate sanitizer API device callback for cuda::memcpy_async.‣Added new command-line option --num-cuda-barriers to override the expected number of cuda::barrier used by the target application.‣Added new command-line options --print-session-details to print session information and --save-session-details to save it to the output file.‣Added support for WSL2.1.16. Updates in 2021.2.3‣Enabled SLS hardening and branch protection for L4T builds.1.17. Updates in 2021.2.2‣Enabled stack canaries with random canary values for L4T builds.1.18. Updates in 2021.2.1‣Added device backtrace for malloc/free errors in CUDA kernels.‣Improved racecheck host memory footprint.1.19. Updates in 2021.2‣Added racecheck and synccheck support for cuda::barrier on Ampere GPUs or newer.‣Added racecheck support for __syncwarp with partial mask.‣Added --launch-count and --launch-skip filtering options. See the Command Line Options documentation for more information.‣--filter and --exclude options have been respectively renamed to --kernel-regex and --kernel-regex-exclude.‣Added support for QNX and Linux aarch64 platforms.‣Added support for CUDA graphs memory nodes.1.20. Updates in 2021.1.1‣Fixed an issue where incorrect line numbers could be shown in errors reports.1.21. Updates in 2021.1‣Added support for allocation padding via the --padding option.‣Added experimental support for NVTX memory API using option --nvtx yes.Please refer to NVTX API for Compute Sanitizer Reference Manual for moreinformation.1.22. Updates in 2020.3.1‣Fixed issue when launching a CUDA graph multiple times.‣Fixed false positives when using cooperative groups synchronization primitives with initcheck and synccheck.1.23. Updates in 2020.3‣Added support for CUDA memory pools and CUDA API reduced serialization.‣Added host backtrace for unused memory reports.1.24. Updates in 2020.2.1‣Fixed crash when loading cubins of size larger than 2 GiB.‣Fixed error detection on systems with multiple GPUs.‣Fixed issue when using CUDA Virtual Memory Management API cuMemSetAccess to remove access to a subset of devices on a system with multiple GPUs.‣Added sanitizer API to translate between sanitizer and CUDA stream handles.1.25. Updates in 2020.2‣Added support for CUDA graphs and CUDA memmap APIs.‣The memory access callback of the sanitizer API has been split into three distinct callbacks corresponding to global, shared and local memory accesses.Release Notes 1.26. Updates in 2020.1.2‣Added sanitizer stream API. This fixes tool crashes when per-thread streams are being used.1.27. Updates in 2020.1.1‣Added support for Windows Hardware-accelerated GPU scheduling‣Added support for tracking child processes spawned by the application launched under the tool via the --target-processes CLI option.1.28. Updates in 2020.1‣Initial release of the Compute Sanitizer (with CUDA 11.0)Updates to the Sanitizer API :‣Added support for per-thread streams‣Added APIs to retrieve the PC and size of a CUDA function or patch‣Added callback for cudaStreamAttachMemAsync‣Added direction to memcpy callback data‣Added stream to memcpy and memset callbacks data‣Added launch callback after syscall setup‣Added visibility field to allocation callback data‣Added PC argument to block entry callback‣Added incoming value to memory access callbacks‣Added threadCount to barrier callbacks‣Added cooperative group flags for barrier and function callbacks1.29. Updates in 2019.1‣Initial release of the Compute Sanitizer API (with CUDA 10.1)‣Applications run much slower under the Compute Sanitizer tools. This may cause some kernel launches to fail with a launch timeout error when running with the Compute Sanitizer enabled.‣Compute Sanitizer tools do not support device backtrace on Maxwell devices (SM5.x).‣Compute Sanitizer tools do not support coredumps on WSL2.‣The memcheck tool does not support CUDA API error checking for API calls made on the GPU using dynamic parallelism.‣The racecheck, synccheck and initcheck tools do not support CUDA dynamic parallelism.‣CUDA dynamic parallelism is not supported when Windows Hardware-accelerated GPU scheduling is enabled.‣Compute Sanitizer tools cannot interoperate with other CUDA developer tools.This includes CUDA coredumps which are automatically disabled by the Compute Sanitizer. They can be enabled instead by using the --generate-coredump option.‣Compute Sanitizer tools do not support IPC memory pools. Using it will result in false positives.‣Compute Sanitizer tools are not supported when SLI is enabled.‣The racecheck tool may print incorrect data for "Current value" when reporting a hazard on a shared memory location where the last access was an atomic operation.This can also impact the severity of this hazard.‣On QNX, when using the --target-processes all option, analyzing shell scripts may hang after the script has completed. End the application using Ctrl-C on the command line in that case.‣The initcheck tool might report false positives for device-to-host cudaMemcpy operations on padded structs that were initialized by a CUDA kernel. The #pragma pack directive can be used to disable the padding as a workaround.‣When a hardware exception occur during a kernel launch that was skipped due to the usage of the kernel-name, kernel-name-exclude, launch-count or launch-skip options, the memcheck tool will not be able to report additionaldetails as an imprecise error.‣The leakcheck feature is disabled under Confidential Computing.Information on supported platforms and GPUs.4.1. Platform SupportT able 1 Platforms supported by Compute Sanitizer4.2. GPU SupportThe compute-sanitizer tools are supported on all CUDA capable GPUs with SM versions 5.0 and above.NoticeALL NVIDIA DESIGN SPECIFICATIONS, REFERENCE BOARDS, FILES, DRAWINGS, DIAGNOSTICS, LISTS, AND OTHER DOCUMENTS (TOGETHER AND SEPARATEL Y, "MATERIALS") ARE BEING PROVIDED "AS IS." NVIDIA MAKES NO WARRANTIES, EXPRESSED, IMPLIED, STATUTORY, OR OTHERWISE WITH RESPECT TO THE MATERIALS, AND EXPRESSL Y DISCLAIMS ALL IMPLIED WARRANTIES OF NONINFRINGEMENT, MERCHANTABILITY, AND FITNESS FOR A PARTICULAR PURPOSE.Information furnished is believed to be accurate and reliable. However, NVIDIA Corporation assumes no responsibility for the consequences of use of such information or for any infringement of patents or other rights of third parties that may result from its use. No license is granted by implication of otherwise under any patent rights of NVIDIA Corporation. Specifications mentioned in this publication are subject to change without notice. This publication supersedes and replaces all other information previously supplied. NVIDIA Corporation products are not authorized as critical components in life support devices or systems without express written approval of NVIDIA Corporation.TrademarksNVIDIA and the NVIDIA logo are trademarks or registered trademarks of NVIDIA Corporation in the U.S. and other countries. Other company and product names may be trademarks of the respective companies with which they are associated. Copyright© 2019-2023 NVIDIA Corporation and affiliates. All rights reserved.This product includes software developed by the Syncro Soft SRL (http:// www.sync.ro/).。
NVIDIA Parallel Nsight 2.0和CUDA 4.0技术更新说明书
NVIDIA Parallel Nsight™ 2.0 and CUDA 4.0 for the Win!Jeff Kiel, Manager of Graphics Tools NVIDIA Corporation, SIGGRAPH 2011AgendaCUDA UpdateNVIDIA Parallel NsightCUDA Debugging and ProfilingGraphics Debugging and ProfilingApplication AnalysisCUDA Update: New in 4.0Easier ProgrammingUse any GPU on any threadC++ new/delete and support for virtual functionsInline PTX assemblyThrust C++ Template Performance Primitives Libraries (sort, reduce, etc.) NVIDIA Performance Primitives library (image/video processing) Faster Multi-GPU ProgrammingUnified Virtual Addressing (UVA)GPU Direct 2.0: GPU peer-to-peer communication technology Developer Tools for Linux and MacOSCUDA-GDBVisual Profiler with Automated Performance AnalysisVisual Studio with Parallel Nsight Integrated development for CPU and GPUBuild Debug ProfileNVIDIA Parallel Nsight for Graphics & Compute Graphics Inspector Real-time inspection of Direct3D API calls Investigate GPU pipeline state See contributing fragments with Pixel History Profile frames to find GPU bottlenecksSystem AnalysisView CPU & GPU events on a single timelineExamine workload dependenciesCUDA, Direct3D, and OpenGL API TraceProfile CUDA kernels using performance counters Free License!CUDA/Graphics Debugger Visual Studio debugging environment GPU Accelerated CUDA and HLSL debugging Examine code executing in parallel Conditional breakpoints, memory viewer, etc.Host + Target (32/64 bit)✓System Analysis✓Graphics InspectorInstall appropriate NVIDIA driverInstall Parallel Nsight Host and MonitorHost + Target (32/64 bit)✓System Analysis✓Graphics Inspector✓CUDA DebuggerInstall appropriate NVIDIA driverInstall Parallel Nsight Host and MonitorConfigure Local Headless Debugging (see User’s Manual)✓System Analysis ✓Graphics Inspector ✓CUDA Debugger ✓Graphics DebuggerNetwork Target (32/64-bit)Host (32/64-bit)Two computers, one with NVIDIA GPUs Install appropriate NVIDIA driver on the Target System Install Parallel Nsight Monitor on the Target System Install Parallel Nsight Host on the Development SystemHost + Target (32/64-bit)Install Parallels Desktop and guest OS Install appropriate NVIDIA drivers Install Parallel Nsight Host and Monitor One computer, two NVIDIA GPUs Full GPU acceleration✓System Analysis✓Graphics Inspector✓CUDA Debugger✓Graphics DebuggerFFT Ocean Demo OverviewBased on “Simulating Ocean Water” by Jerry Tenssendorf Statistical model, not physics basedGenerates wave distribution in frequency domain on GPUInverse FFT on GPUMovies: Large height map (2048x2048)Games on CPU: Small height map (64x64)GPU Based: Medium height map (512x512)The ocean surface is composed by enormous simple wavesEach simple wave is a hybrid sine wave (Gerstner wave)A mass point on the surface is doing vertical circular motionScene Rendering ComponentsWave Simulation in CUDARendering in DX11Demo: Launching…Start Nsight MonitorConfigure Parallel Nsight Project SettingsLaunch Your ApplicationDemo: Graphics Debugging -HUDDemo: HUD Showing 2x2 TextureDemo: HUD in Graphics InspectorDemo: HUD Render Target, Depth & StencilDemo: Host Frames PageDemo: Draw Call PageDemo: Texture ViewerDemo: Pixel Shader State InspectorDemo: Buffer InspectorDemo: Output Merger InspectorDemo: Pixel HistoryDemo: Shader Debugger BreakpointView all graphics resources at a glance Version 2.0Numerous usability and workflow improvementsGraphics profiler performance and accuracyDriver independenceStability improvementsSupport for latest drivers and hardwareVersion 2.0CUDA Toolkit 4.0 SupportFull Visual Studio 2010 Platform Support Tesla Compute Cluster (TCC) Analysis PTX Assembly DebuggingAttach to ProcessDerived Metrics and Experiments Concurrent Kernel TraceRuntime API TraceAdvanced Conditional Breakpoints Support for latest drivers hardwareWrap Up…Thank You!Call to action!Download Parallel Nsight and try it outSend us feedback on what features you find important Come talk to us here at SIGGRAPHContact us on the NVIDIA Developer Forums/index.php?showforum=191。
GPGPU
• •
•
•
•
9
提 纲
• GPGPU简介 GPGPU简介
–为什么GPGPU会被引入高性能计算 为什么GPGPU GPGPU会被引入高性能计算 –GPGPU的应用领域 GPGPU的应用领域
• • • •
主流GPGPU 主流GPGPU介绍 GPGPU介绍 曙光与GPGPU 曙光与GPGPU GPGPU并行编程模式概述 GPGPU并行编程模式概述 CUDA并行编程模式详细介绍 CUDA并行编程模式详细介绍
dual slots 全长卡（X16 PCI-e） 40db桌面系统 187.8W 800W
Nvidia Tesla的开发环境 Tesla的开发环境
• CUDA（统一计算设备架构）
– Compute Unified Device Architecture – 在nVIDIA GT200系列显卡和Tesla系列通 用计算系统上 C 语言的函式库来编写应用 程序的软件开发环境， – CUDA 主要分为 Library，runtime和 Driver 三个部分
GPGPU所引领的高性能计算 GPGPU所引领的高性能计算
技术支持中心 2010-01-28
提 纲
• GPGPU简介 GPGPU简介
–为什么GPGPU会被引入高性能计算 为什么GPGPU GPGPU会被引入高性能计算 –GPGPU的应用领域 GPGPU的应用领域
• • • •
主流GPGPU 主流GPGPU介绍 GPGPU介绍 曙光与GPGPU 曙光与GPGPU GPGPU并行编程模式概述 GPGPU并行编程模式概述 CUDA并行编程模式详细介绍 CUDA并行编程模式详细介绍
15
复杂多相流动分子动力学
• 分子动力学(molecular dynamics，MD)模拟是认为分子之 间作用力遵守牛顿力学的一种科学计算方法，现已广泛应 用到生物、医药、材料、能源、机电的等领域中。 • 中国科学院过程工程研究所多相复杂系统国家重点实验室 进行了GPGPU上的分子动力学(MD)模拟。
信创的基础知识培训课件
信创的基础知识培训课件目录CONTENTS 信创的基本概念信创的产业链信创的现状与未来■信创的基本概念口信创的起源-2016年3月4日，24家专业从事软硬件关键技术研究及应用的国内单位，共同发起成立了一个非营利性社会组织，并将其命名为“信息技术应用创新工作委员会”,简称“信创工委会”。
这是“信创”这个词的来源。
- “信创工委会”成立后不久，全国各地相继又成立了大量的信创产业联盟，共同催生了庞大的信息技术应用创新产业，也被称为“信创产业”,简称“信创”。
- “信创”的核心目的，是为了实现信息技术产业的完全自主可控。
- “信创”,以前也被称为“安可(安全可控)”。
单位类别单位名称集成厂商(10家)中国软件、太极股份、航天信息、浪潮软件、东华软件、神舟航天软件、东软集团、神州信息、同方股份、华宇软件第三方机构(4家)国家工业信息安全发展研究中心、中国电子技术标准化研究院、工业和信息化部电子第五研究所、中国电子信息产业发展研究院互联网厂商(3家)阿里云、金山软件、HW 技术高等院校(2家)北京航空航天大学、北京理工大学信创的基本概念口信创的起源“信创工委会”理事单位口信创的发展历程(“863”、“973”、“核高基”)-1986年3月3日，王大珩、王淦昌、杨嘉墀、陈芳允四位“两弹一星元勋”致信国家领导人，提出全面追踪世界高科技的发展并制定中国发展高科技计划的建议和设想。
3月5日，领导人亲自批准启动，并调拨100亿人民币作为项目专项资金。
这就是著名的“863 项目”,标志着中国自主创新开始起步。
-1997年，由国家科技领导小组第三次会议决定实施“973”计划，解决国家经济与社会发展中的重大科学问题，以提高我国自主创新能力和解决重大问题的能力。
-2006-2013年，预研阶段，国家启动“核高基01事项”。
核高基就是“核心电子器件、高端通用芯片及基础软件产品”的简称，是2006年国务院发布的《国家中长期科学和技术发展规划纲要(2006-2020年)》中与载人航天、探月工程并列的16个重大科技专项之一。
曙光 天阔 A840r-H 服务器 说明书
曙光天阔A840r-H 服务器是曙光公司最新推出的一款性能优异,稳定可靠,配置灵活的新一代四路机架式服务器。
它支持4颗新一代AMD 8000系列处理器，配备更高性能的大容量DDR2内存模组，为企业级应用提供了充分的性能保障。
曙光天阔A840r-H 服务器采用了NVIDIA高性能服务器芯片组，提供高速HyperTransport 直连架构，支持高速DDR2内存，可以极大地提高整机性能和运行效率。
A840r-H 服务器提供了16根DIMM 插槽。
支持热插拔硬盘，可选的RAID 配置能更好的保证数据的安全性。
A840r-H 服务器不仅提供了超群的高性能和高可靠性，而且为将来的服务器系统平台升级预留了空间。
曙光天阔A840r-H 服务器采用4U 机架式设计，在有限的空间为用户提供了强大的功能。
独特高密机架式服务器散热结构设计，能稳定运行Windows、Redhat Linux、SUSE linux 等多种32位和64位主流操作系统，是能适应多种重要任务环境的新一代IA 架构服务器。
它具有处理速度快、可用性强、易管理、可靠性高等特点。
系统还提供多种部件独特功能，包括大功率服务器专用电源，支持自动负载平衡的高速网卡，配置了曙光灵动散热系统等。
概述天阔A840r-H 服务器曙光天阔服务器 A840r-H典型应用A840r-H服务器是一款性能超群、高稳定性的企业级产品，能够充分满足电信、生物信息、石油、气象、化工、医药、流体分析、力学分析、有限元分析、高性能计算等苛刻环境的要求，完全满足企业核心数据库服务、中间件、ERP等核心运用。
专业化的高密度设计，使A840r-H在大规模科学计算和数据处理领域性能尤为突出，既可做为TFlops级超级服务器的专用高性能计算节点，又可作为后台数据库服务器承担电信级海量处理。
是大型企业、政府、军队、医药、科研、教育等机构的首选产品。
z核心数据库A840r-H服务器在HyperTransport超传输总线的支持下，彻底消除了总线带宽的瓶颈， CPU内嵌大容量缓存、双通道内存控制器技术，优异的内存带宽,可应用于金融、证券、交通、邮电、通信等对服务器性能、可靠性要求非常苛刻的行业。
SIMATIC 过程控制系统 PCS 7 维护站 V9.1 功能手册说明书
3.12
PAM 站 ........................................................................................................................ 39
4 附加文档 ........................................................................................................................................ 43
维护站 V9.1
功能手册, 02/2021, A5E49490728-AA
3
目录
5.7.4
如何为 MS 或 OS/MS 客户端组态 PC 站......................................................................... 56
5.8 5.8.1 5.8.2 5.8.3 5.8.4
3.2
具有诊断功能的对象.................................................................................................... 20
3.3
“PC 站”区域 ............................................................................................................... 21
3.10
具有多个 OS 单工作站系统的工厂组态中的 MS ............................................................ 34
hpc_曙光(SharePoint OA解决方案)
机箱结构-整体示意图
GPU卡2 卡
主板2 主板 GPU卡1 卡
硬盘 主板1 主板 电源1 电源 电源2 电源 风扇 机箱前部
整机方案细节描述—主板规格
• 主板技术规格
– Form Factor：大约 16.7”x6.8” （42.3cm x 17.3cm） – CPU:最高支持2颗AMD barcelona or shanghai 处理器 – Chipset:Nvidia nForce3600 – 内存：16 DIMM插槽，支持DDR2 533/667 ECC REG – LAN:2 Gigabit LAN – Infiniband: Mellanox InfiniHost III Lx DDR MT25204A0-FCC-D single port – SATA:4-SATA2 Support Raid 0,1,5 – PCIE: 1全长全高 PCI-Ex16 (支持双卡宽度，每机 箱支持2片卡） 图片仅供参考 – IPMI 2.0
由众多部件组成，具有运算速度快、存储容量大、可 靠性高的特性。
也称为：巨型计算机、超级计算 机 目前任何高性能计算和超级计算 都离不开使用并行技术，所以高 性能计算机肯定是并行计算机。
1.2 流行的高性能计算机架构
并行向量机 SMP DSM（NUMA） MPP，节点可以是单处理器的节点，也可以是SMP， DSM Cluster 混合架构
登陆管理软件
IP：10.0.0.1 子网掩码：255.255.255.0 用户：administrator 密码：password
DS6310系列磁盘阵列的管理端口支持虚拟IP技术，可将一台磁盘阵列上的 两个控制器的管理端口绑定在一个虚拟的IP上，虚拟IP与两个控制器的IP无关， 他们可以设置在一个网段中，也可以不在。默认虚拟IP为10.0.0.1，子网掩码 255.255.255.0，控制器默认真实IP为10.0.0.2/3，子网掩码：255.255.255.0。 登陆后会弹出安全警报，点击“是”才能正常登陆管理软件。
AMDFireProS10000高密集高性能服务器显卡
AMD FirePro™ S10000 高密集、高性能服务器显卡
图形虚拟化的领先优势
AMD FirePro S 系列服务器显卡兼容来自 Citrix®、 VMware® 和 Microsoft® 的领先虚拟化技术；这些技术可 以交付图形加速的虚拟机。结合 RemoteFX 使用时，一 张安装在数据中心的 AMD FirePro S10000 服务器显卡可 以驱动大量远程并发用户的计算会话。在 RemoteFX 的 帮助下，所有用户只需要连接一个 PC 客户端设备或一 个零客户门户。不要求在每个终端用户的工作区域安装 特殊的硬件 — 只要有网络连接、显示器、键盘和鼠标即 可。用户可以无缝使用业务生产力应用程序、视频、有 丰富图形的操作系统界面、入门级 CAD 及媒体娱乐应用 程序。
AMD FirePro™ 能满足苛刻的集中式计算需求。配合一个 统一驱动程序，AMD FirePro™ S10000 服务器显卡可为 信息技术和数据中心经理提供一个强大、灵活和可扩 展的解决方案，能够支持远程显卡和虚拟桌面基础架构 (VDI) 部署、渲染农场和超级计算集群。
特性
28nm 次世代图形核心建筑
优势
NVIDIARTX3050值得入手吗NVIDIARTX3050显卡详细评测
NVIDIARTX3050值得⼊⼿吗NVIDIARTX3050显卡详细评测NVIDIA RTX3050怎么样？好不好⽤？值得购买吗？下⾯⼩编带来NVIDIA RTX3050显卡详细评测，希望对⼤家有所帮助。
NVIDIA RTX3050显卡详细评测：⼀、前⾔：传说中1899元能买到的显卡如果按照GPU的发展规律，2019年发布的⾸发价1899元的GTX 1660 Super，在3年之后的今天理应掉到1000元以下。
但事实情况是，京东平台上，全新的GTX 1660 Super售价普遍都在3000元以上，游戏玩家想要买⼀块能玩游戏的显卡何其之难！也许是⽼黄看透了玩家的痛苦！对于今天发布的RTX 3050，NVIDIA对AIC⼚商有苛刻的要求，那就是必须要有1899元的型号并且有充⾜的货源让正常的游戏玩家能够买到原价的显卡。
如果AIC都能够严格执⾏的话，或许能在⼀定程度上缓解游戏玩家买不到显卡的尴尬局⾯。
RTX 3050采⽤的是GA106-150核⼼，这是⽬前为⽌最为精简的安培核⼼。
只有2组GPC，每组GPC内含5组TPC、10组 SM 单元、1280个流处理器、外加16个ROP。
总计则是10组TPC、20组SM单元、2560个流处理器和32个ROP，另外还有80个第三代Tensor Core和20个第⼆代RT Core。
下图是NVIDIA GeForce RTX 3050的详细规格参数：RTX 3050标配了8GB GDDR6显存，位宽128bit，频率14GHz，带宽224GB/s。
另外，虽然第三代Tensor Core虽然数量较少，但是效能⼏乎是第⼆代的2倍。
RTX 3050拥有80个Tensor Core，等效于图灵核⼼的160个Tensor Core。
关于安培GPU流出器效能的说明：RTX 3050流处理器数量⽐GTX 1660 Super多出了1152个，但性能与之相当，看起来似乎不合常理。
中国计算机发展史
1958年，中科院计算所研制成功我国第一台小型电子管通用计算机103机(八一型),标志着我国第一台电子计算机的诞生。
1965年，中科院计算所研制成功第一台大型晶体管计算机109乙，之后推出109丙机，该机为两弹试验中发挥了重要作用；1974年，清华大学等单位联合设计、研制成功采用集成电路的DJS-130小型计算机，运算速度达每秒100万次；1983年，国防科技大学研制成功运算速度每秒上亿次的银河-I 巨型机，这是我国高速计算机研制的一个重要里程碑；1985年，电子工业部计算机管理局研制成功与IBM PC机兼容的长城0520CH微机。
1992年，国防科技大学研究出银河-II通用并行巨型机，峰值速度达每秒4亿次浮点运算(相当于每秒10亿次基本运算操作)，为共享主存储器的四处理机向量机，其向量中央处理机是采用中小规模集成电路自行设计的，总体上达到80年代中后期国际先进水平。
它主要用于中期天气预报；1993年，国家智能计算机研究开发中心（后成立北京市曙光计算机公司）研制成功曙光一号全对称共享存储多处理机，这是国内首次以基于超大规模集成电路的通用微处理器芯片和标准UNIX操作系统设计开发的并行计算机；1995年，曙光公司又推出了国内第一台具有大规模并行处理机(MPP)结构的并行机曙光1000(含36个处理机)，峰值速度每秒25亿次浮点运算，实际运算速度上了每秒10亿次浮点运算这一高性能台阶。
曙光1000与美国Intel公司1990年推出的大规模并行机体系结构与实现技术相近，与国外的差距缩小到5年左右。
1997年，国防科大研制成功银河-III百亿次并行巨型计算机系统，采用可扩展分布共享存储并行处理体系结构，由130多个处理结点组成，峰值性能为每秒130亿次浮点运算，系统综合技术达到90年代中期国际先进水平。
1997至1999年，曙光公司先后在市场上推出具有机群结构(Cluster)的曙光1000A，曙光2000-I，曙光2000-II超级服务器，峰值计算速度已突破每秒1000亿次浮点运算，机器规模已超过160个处理机，1999年，国家并行计算机工程技术研究中心研制的神威I计算机通过了国家级验收，并在国家气象中心投入运行。
vgpu桌面解决方案[工作范文]
![vgpu桌面解决方案[工作范文]](https://img.taocdn.com/s3/m/7ae9e292dd3383c4bb4cd2cd.png)
vgpu桌面解决方案篇一：Citrix vGPU部署文档Citrix vGPU 部署方案福建升腾资讯有限公司Fujian Centerm Information Co.,Ltd第一章概述本文档主要是指导区域支持进行Citrix vGPU环境搭建，本文档主要是涉及到在搭建过程中需要注意点，在整体部署过程，可以参考《VDBOX生产部署文档》。
第二章部署前准备硬件准备软件准备软件准备包括：NVIDIA GIRD卡管理驱动，WIN7桌面vGPU 驱动以及Citrix整体环境搭建需要的软件包。
NVIDIA软件包：注：该软件包在NVIDIA官网可以下载，需要注意相应的版本。
Citrix 安装包：XenServer:vGPU的解决方案中，服务器虚拟化目前只支持XenServer SP1版本（如果之前XenServer有安装过XSE62TP001 hotfix，请重装XenServer ),推荐采用XenServer XenDesktop：vGPU的解决方案中XenDesktop 版本必须是XenDesktop （发布版，Product version ）或XenDesktop 以后发布的版本。
推荐采用第三章硬件部署GRID显卡认证服务器型号注：由于支持Citrix vGPU类型的服务器在递增，故如果需要了解相应服务器是否支持Citrix vGPU，可以向总部反馈，进行查询。
服务器BIOS设置以及供电部分早期生产的服务器，由于Bios相对较旧，故可能出现兼容等问题，建议参考列表对照，以确保服务器的bios要新于HCL列表所列。
确保物理服务器上，性能已经设定为Max Performance （每家服务器设定不同，请联系对应厂商）BIOS 设定中“Enable >4G Decode”或“Enable 64-bit MMIO”，选择“禁用”。
（如不禁用会出现对应VM无法正常启动等异常问题）XenServer 则无须关闭测设定。
GPU也能虚拟化
GPU也能虚拟化作者：郭涛来源：《中国计算机报》2014年第04期虚拟化技术改变了数据中心资源的分配和使用方式，也让桌面系统、智能移动终端的使用体验变得更加流畅，惟独在以GPU应用为核心的图形设计工作中，人们还无法享受到虚拟化带来的诸多便利。
不过，在曙光、NVIDIA和思杰共同描绘的“云图”中，这种情况将彻底得到改观。
曙光推出的基于GRID和思杰XenDesktop技术的图形云计算产品——云图W760-G10，解决了GPU硬件虚拟化的技术难题。
这也是国内首款专用的图形云计算产品。
不是产品是方案以前，主要用于图形计算的GPU处理器一直无法很好地实现硬件虚拟化，当然也就谈不上按需调配GPU资源。
NVIDIA公司闯过了GPU虚拟化这一关，其Kepler架构系列GPU冲破了GPU硬件虚拟化的技术瓶颈，将硬件虚拟化扩展到GPU层面，从而为图形化计算工具的云化奠定了基础。
NVIDIA的虚拟GPU技术、思杰的桌面虚拟化技术，再加上曙光在服务器以及系统整合方面的优势，云图W760-G10应运而生。
云图W760-G10采用NVIDIA的Kepler GPU架构，其虚拟化功能允许GPU同时被多名用户共享，并以超快的流式显示功能消除了迟滞现象，使得云计算架构的优势可以在图形计算上完全发挥出来。
“我们联合NVIDIA和思杰，打通了云计算、图形计算和虚拟化应用三者之间的通路。
”曙光首席运营官王正福表示，“云图W760-G10不仅仅是一台服务器，而是一个整体解决方案，它可以全面满足医疗、教育、军工、装饰装修等行业用户在图形设计方面的需求。
”应用更灵活从性能表现看，云图W760-G10内置两颗10核心CPU，总共20个物理核心和40个超线程核心，每核心主频3.0GHz，可加速到3.6GHz，内存可扩展至512GB，为用户提供了一个强大的计算资源池；在图形虚拟化方面，云图W760-G10内置4颗基于NVIDIA公司CUDA技术的GPU核心，总共提供6144个图形运算核心作为图形虚拟化池；在存储方面，云图W760-G10提供了8个SAS/SATA扩展盘位，最大存储容量可达64TB，同时还配备了8Gb/s FC接口，可以实现存储容量的无限扩展。
NVIDIA Data Center GPU Driver版本高亮文档说明书
NVIDIA Data Center GPU Driver version 470.182.03 (Linux) / 474.30 (Windows)Release NotesTable of Contents Chapter 1. Version Highlights (1)1.1. Software Versions (1)1.2. Fixed Issues (2)1.3. Known Issues (3)Chapter 2. Virtualization (5)Chapter 3. Hardware and Software Support (7)Chapter 1.Version HighlightsThis section provides highlights of the NVIDIA Data Center GPU R470 Driver (version 470.182.03 Linux and 474.30 Windows).For changes related to the 470 release of the NVIDIA display driver, review the file "NVIDIA_Changelog" available in the .run installer packages.‣Linux driver release date: 3/30/2023‣Windows driver release date: 3/30/20231.1. Software VersionsFor this release, the software versions are listed below.‣CUDA Toolkit 11: 11.4Note that starting with CUDA 11, individual components of the toolkit are versioned independently. For a full list of the individual versioned components (for example, nvcc, CUDA libraries and so on), see the CUDA Toolkit Release Notes‣NVIDIA Data Center GPU Driver: 470.182.03 (Linux) / 474.30 (Windows)‣Fabric Manager: 470.182.03 (Use nv-fabricmanager -v)‣GPU VBIOS:‣HGX A100 PG506‣92.00.45.00.03 SKU200 40GB air cooling (lidless)‣92.00.45.00.04 SKU202 40GB hybrid cooling (lidded)‣92.00.45.00.05 SKU210 80GB air cooling (lidless)‣92.00.45.00.06 SKU212 80GB hybrid cooling (lidded)‣HGX A100 PG510‣92.00.81.00.01 SKU200 40GB air cooling (lidless)‣92.00.81.00.02 SKU202 40GB hybrid cooling (lidded)‣92.00.81.00.04 SKU210 80GB air cooling (lidless)‣92.00.81.00.05 SKU212 80GB hybrid cooling (lidded)‣HGX A800 PG506‣92.00.A4.00.01 SKU215 80GB air cooling (lidless)‣HGX A800 PG510‣92.00.A4.00.05 SKU215 80GB air cooling (lidless)‣A100 PCIe P1001 SKU230‣92.00.90.00.04 (NVIDIA A100 PCIe)‣A800 PCIe P1001‣92.00.A4.00.0C 40 GB SKU203 PCIe‣92.00.A4.00.0D 80 GB SKU235 PCIe‣NVSwitch VBIOS: 92.10.14.00.01‣NVFlash: 5.791Due to a revision lock between the VBIOS and driver, VBIOS versions >= 92.00.18.00.00 must use corresponding drivers >= 450.36.01. Older VBIOS versions will work with newer drivers.For more information on getting started with the NVIDIA Fabric Manager on NVSwitch-based systems (for example, NVIDIA HGX A100), refer to the Fabric Manager User Guide.1.2. Fixed Issues‣Resolved a kernel panic on A100 when using both MIG and DCGM. Pascal GPU page faults were hitting a NULL pointer dereference in the UVM driver while there was not enough system memory available to handle the faults.‣Resolved an issue that sometimes caused abnormal BAR1 memory usage.‣In the L1C submodule when the clock is gated, there is a corner case where the BLCG controller was not woken up from sleep state when an external submodule wants to use L1C. This was fixed by Switching the PROD value to disable L1C BLCG will not cause the hang in the chip until some other event wakes up the BLCG FSM.‣nvidia-smi -q returns strange values on A100 and A16 GPUs.‣Fixed an issue where the NVIDIA Linux GPU kernel driver was calling the Linux kernel scheduler while holding a lock with preemption disabled during event notification.‣In the code path related to allocating virtual address space, a call to reallocate memory for tracking structures was allocating less memory than needed, resulting ina potential memory trampler. The size of the reallocation is now correctly calculated.‣Fixed the Race condition between cudaFreeAsync() and cudaDeviceSynchronize() which were being hit if device sync is used instead of stream sync in multi threaded app. Now a Lock is being held for the appropriate duration so that a subpool cannot be modified during a very small window which triggers an assert as the subpoolshould have been empty due to the free code.1.3. Known IssuesGeneral‣The GPU driver build system might not pick the Module.symvers file, produced when building the ofa_kernel module from MLNX_OFED, from the right subdirectory.Because of that, nvidia_peermem.ko does not have the right kernel symbol versions for the APIs exported by the IB core driver, and therefore it does not load correctly.That happens when using MLNX_OFED 5.5 or newer on a Linux Arm64 or ppc64le platform.To work around this issue, perform the following:1.Verify that nvidia_peermem.ko does not load correctly.2.Uninstall old MLNX_OFED if one was installed.3.Manually remove /usr/src/ofa_kernel/default if one exists.4.Install MLNX_OFED5.5 or newer.5.Manually create a soft link:/usr/src/ofa_kernel/default -> /usr/src/ofa_kernel/$(uname -m)/$(uname -r)6.Reinstall the GPU driver.‣On HGX A800 8-GPU systems, the nvswitch-audit tool will report 12 NVLinks per GPU. This is a switch configuration report and does not reflect the true number of NVLink interfaces available per-GPU, which remains 8.‣Combining A800 and A100 SXM modules in a single server is not currently supported with this driver version.‣Combining A800 and A100 PCIe with NVLink is not fully tested.‣By default, Fabric Manager runs as a systemd service. If using DAEMONIZE=0 in the Fabric Manager configuration file, then the following steps may be required.1.Disable FM service from auto starting. (systemctl disable nvidia-fabricmanager)2.Once the system is booted, manually start FM process. (/usr/bin/nv-fabricmanager -c /usr/share/nvidia/nvswitch/fabricmanager.cfg). Note,since the process is not a daemon, the SSH/Shell prompt will not be returned (use another SSH shell for other activities or run FM as a background task).‣When installing a driver on SLES15 or openSUSE15 that previously had an R515 driver installed, users need to run the following command afterwards to finalize the installation:sudo zypper install --force nvidia-gfxG05-kmp-defaultWithout doing this, users may see the kernel objects as missing.‣If you encounter an error on RHEL7 when installing with cuda-drivers-fabricmanager packages, use the following alternate instructions. For example:If you are upgrading from a different branch, for example to driver 470.141.03:new_version=470.141.03sudo yum swap nvidia-driver-latest-dkms nvidia-driver-latest-dkms-${new_version} sudo yum install nvidia-fabric-manager-${new_version}GPU Performance CountersThe use of developer tools from NVIDIA that access various performance counters requires administrator privileges. See this note for more details. For example, reading NVLink utilization metrics from nvidia-smi (nvidia-smi nvlink -g 0) would require administrator privileges.NoScanout ModeNoScanout mode is no longer supported on NVIDIA Data Center GPU products. If NoScanout mode was previously used, then the following line in the “screen” sectionof /etc/X11/xorg.conf should be removed to ensure that X server starts on data center products:Option "UseDisplayDevice" "None"NVIDIA Data Center GPU products now support one display of up to 4K resolution. Unified Memory SupportCUDA and unified memory is not supported when used with Linux power management states S3/S4.IMPU FRU for Volta GPUsThe driver does not support the IPMI FRU multi-record information structure for NVLink. See the Design Guide for Tesla P100 and Tesla V100-SXM2 for more information. OpenCL 3.0 Known Issues‣Device-Side-Enqueue related queries may return 0 values, although corresponding built-ins can be safely used by kernel. This is in accordance with conformancerequirements described at https:///registry/OpenCL/specs/3.0-unified/html/OpenCL_API.html#opencl-3.0-backwardscompatibility‣Shared virtual memory—the current implementation of shared virtual memory is limited to 64-bit platforms only.Chapter 2.VirtualizationTo make use of GPU passthrough with virtual machines running Windows and Linux, the hardware platform must support the following features:‣ A CPU with hardware-assisted instruction set virtualization: Intel VT-x or AMD-V.‣Platform support for I/O DMA remapping.‣On Intel platforms the DMA remapper technology is called Intel VT-d.‣On AMD platforms it is called AMD IOMMU.Support for these features varies by processor family, product, and system, and should be verified at the manufacturer's website.The following hypervisors are supported for virtualization:Tesla products now support one display of up to 4K resolution.The following GPUs are supported for device passthrough for virtualization:VirtualizationChapter 3.Hardware and SoftwareSupportSupport for these features varies by processor family, product, and system, and should be verified at the manufacturer's website.Supported Operating Systems for NVIDIA Data Center GPUsThe Release 470 driver is supported on the following operating systems:‣Windows x86_64 operating systems:‣Microsoft Windows® Server 2022‣Microsoft Windows® Server 2019‣Microsoft Windows® Server 2016‣Microsoft Windows® 11‣Microsoft Windows® 10‣The following table summarizes the supported Linux 64-bit distributions. For a complete list of distributions, kernel versions supported, see the CUDA Linux System Requirements documentation.Hardware and Software SupportSupported Operating Systems and CPU Configurations for NVIDIA HGXA100 and HGX A800The Release 470 driver is validated with NVIDIA HGX A100 and HGX A800 on the following operating systems and CPU configurations:‣Linux 64-bit distributions:‣Red Hat Enterprise Linux 8.7 (in 4/8/16-GPU configurations)‣Red Hat Enterprise Linux 7.9 (in 4/8/16-GPU configurations)‣Rocky Linux 8.7 (in 4/8/16-GPU configurations)‣CentOS Linux 7.9 (in 4/8/16-GPU configurations)‣Ubuntu 18.04.6 LTS (in 4/8/16-GPU configurations)‣SUSE SLES 15.4 (in 4/8/16-GPU configurations)‣Windows 64-bit distributions:‣Windows Server 2019 (in 1/2/4/8-GPU configurations; 16-GPU configurations are currently not supported)Windows is supported only in shared NVSwitch virtualization configurations.‣CPU Configurations:‣AMD Rome in PCIe Gen4 mode‣Intel Skylake/Cascade Lake (4-socket) in PCIe Gen3 modeSupported Virtualization ConfigurationsThe Release 470 driver is validated with NVIDIA HGX A100 and HGX A800 on the following configurations:‣Passthrough (full visibility of GPUs and NVSwitches to guest VMs):‣8-GPU configurations with Ubuntu 18.04.4 LTS‣Shared NVSwitch (guest VMs only have visibility of GPUs and full NVLink bandwidth between GPUs in the same guest VM):‣16-GPU configurations with Ubuntu 18.04.4 LTS‣1/2/4/8-GPU configurations with Windows x86_64 operating systems:‣Microsoft Windows® Server 2019‣Microsoft Windows® 10API SupportThis release supports the following APIs:‣NVIDIA® CUDA® 11.4 for NVIDIA® Kepler TM, Maxwell TM, Pascal TM, Volta TM, Turing TM, and NVIDIA Ampere architecture GPUs‣OpenGL® 4.6‣Vulkan® 1.2‣DirectX 11‣DirectX 12 (Windows 10)‣Open Computing Language (OpenCL TM software) 3.0Note that for using graphics APIs on Windows (such as OpenGL, Vulkan, DirectX 11,and DirectX 12) or any WDDM 2.0+ based functionality on Data Center GPUs, vGPU is required. See the vGPU documentation for more information.Supported NVIDIA Data Center GPUsThe NVIDIA Data Center GPU driver package is designed for systems that have one or more Data Center GPU products installed. This release of the driver supports CUDA C/C+ + applications and libraries that rely on the CUDA C Runtime and/or CUDA Driver API. Attention: Release 470 will be the last driver branch to support Data Center GPUs based on the NVIDIA Kepler architecture. This includes discontinued support for the following compute capabilities:‣sm_30 (NVIDIA Kepler)‣sm_32 (NVIDIA Kepler)‣sm_35 (NVIDIA Kepler)‣sm_37 (NVIDIA Kepler)For more information on GPU products and compute capability, see https:// /cuda-gpus.NoticeThis document is provided for information purposes only and shall not be regarded as a warranty of a certain functionality, condition, or quality of a product. NVIDIA Corporation (“NVIDIA”) makes no representations or warranties, expressed or implied, as to the accuracy or completeness of the information contained in this document and assumes no responsibility for any errors contained herein. NVIDIA shall have no liability for the consequences or use of such information or for any infringement of patents or other rights of third parties that may result from its use. This document is not a commitment to develop, release, or deliver any Material (defined below), code, or functionality.NVIDIA reserves the right to make corrections, modifications, enhancements, improvements, and any other changes to this document, at any time without notice.Customer should obtain the latest relevant information before placing orders and should verify that such information is current and complete.NVIDIA products are sold subject to the NVIDIA standard terms and conditions of sale supplied at the time of order acknowledgement, unless otherwise agreed in an individual sales agreement signed by authorized representatives of NVIDIA and customer (“Terms of Sale”). NVIDIA hereby expressly objects to applying any customer general terms and conditions with regards to the purchase of the NVIDIA product referenced in this document. No contractual obligations are formed either directly or indirectly by this document.NVIDIA products are not designed, authorized, or warranted to be suitable for use in medical, military, aircraft, space, or life support equipment, nor in applications where failure or malfunction of the NVIDIA product can reasonably be expected to result in personal injury, death, or property or environmental damage. NVIDIA accepts no liability for inclusion and/or use of NVIDIA products in such equipment or applications and therefore such inclusion and/or use is at customer’s own risk.NVIDIA makes no representation or warranty that products based on this document will be suitable for any specified use. Testing of all parameters of each product is not necessarily performed by NVIDIA. It is customer’s sole responsibility to evaluate and determine the applicability of any information contained in this document, ensure the product is suitable and fit for the application planned by customer, and perform 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 document. NVIDIA accepts no 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 document or (ii) customer product designs.No license, either expressed or implied, is granted under any NVIDIA patent right, copyright, or other NVIDIA intellectual property right under this document. Information published by NVIDIA regarding third-party products or services does not constitute a license from NVIDIA to use such products or services or a warranty or endorsement thereof. Use of such information may require a license from a third party under the patents or other intellectual property rights of the third party, or a license from NVIDIA under the patents or other intellectual property rights of NVIDIA.Reproduction of information in this document is permissible only if approved in advance by NVIDIA in writing, reproduced without alteration and in full compliance with all applicable export laws and regulations, and accompanied by all associated conditions, limitations, and notices.THIS DOCUMENT AND ALL NVIDIA DESIGN SPECIFICATIONS, REFERENCE BOARDS, FILES, DRAWINGS, DIAGNOSTICS, LISTS, AND OTHER DOCUMENTS (TOGETHER AND SEPARATELY, “MATERIALS”) ARE BEING PROVIDED “AS IS.” NVIDIA MAKES NO WARRANTIES, EXPRESSED, IMPLIED, STATUTORY, OR OTHERWISE WITH RESPECT TO THE MATERIALS, AND EXPRESSLY DISCLAIMS ALL IMPLIED WARRANTIES OF NONINFRINGEMENT, MERCHANTABILITY, AND FITNESS FOR A PARTICULAR PURPOSE. TO THE EXTENT NOT PROHIBITED BY LAW, IN NO EVENT WILL NVIDIA BE LIABLE FOR ANY DAMAGES, INCLUDING WITHOUT LIMITATION ANY DIRECT, INDIRECT, SPECIAL, INCIDENTAL, PUNITIVE, OR CONSEQUENTIAL DAMAGES, HOWEVER CAUSED AND REGARDLESS OF THE THEORY OF LIABILITY, ARISING OUT OF ANY USE OF THIS DOCUMENT, EVEN IF NVIDIA HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. Notwithstanding any damages that customer might incur for any reason whatsoever, NVIDIA’s aggregate and cumulative liability towards customer for the products described herein shall be limited in accordance with the Terms of Sale for the product.TrademarksNVIDIA and the NVIDIA logo 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© 2023 NVIDIA Corporation & affiliates. All rights reserved.。
NVIDIA Data Center GPU Driver 515.48.07 (Linux)
NVIDIA Data Center GPU Driver version 515.48.07 (Linux) / 516.31 (Windows)Release NotesTable of Contents Chapter 1. Version Highlights (1)1.1. Software Versions (1)1.2. Known Issues (2)Chapter 2. Virtualization (4)Chapter 3. Hardware and Software Support (6)Chapter 1.Version HighlightsThis section provides highlights of the NVIDIA Data Center GPU R515 Driver (version 515.48.07 Linux and 516.31 Windows).For changes related to the 515 release of the NVIDIA display driver, review the file "NVIDIA_Changelog" available in the .run installer packages.‣Linux driver release date: 06/06/2022‣Windows driver release date: 06/06/20221.1. Software VersionsFor this release, the software versions are as follows:‣CUDA Toolkit 11: 11.7Note that starting with CUDA 11, individual components of the toolkit are versionedindependently. For a full list of the individual versioned components (for example, nvcc, CUDA libraries, and so on), see the CUDA Toolkit Release Notes.‣NVIDIA Data Center GPU Driver: 515.48.07 (Linux) / 516.31 (Windows)‣Fabric Manager: 515.48.07 (Use nv-fabricmanager -v)‣GPU VBIOS:‣92.00.19.00.01 (NVIDIA A100 SKU200 with heatsink for NVIDIA HGX A100 8-way and 4-way)‣92.00.19.00.02 (NVIDIA A100 SKU202 w/o heatsink for NVIDIA HGX A100 4-way)‣NVSwitch VBIOS: 92.10.14.00.01‣NVFlash: 5.641Due to a revision lock between the VBIOS and driver, VBIOS versions >= 92.00.18.00.00 must use corresponding drivers >= 450.36.01. Older VBIOS versions will work with newer drivers. For more information on getting started with the NVIDIA Fabric Manager on NVSwitch-based systems (for example, NVIDIA HGX A100), refer to the Fabric Manager User Guide.1.2. Known IssuesGeneral‣When upgrading the driver from 515.43.04 (shipped with 11.7.0) on SLES15 or openSUSE15, users need to run the following command afterwards to finalize the installation:sudo zypper install --force nvidia-gfxG05-kmp-defaultWithout doing this, users will see the kernel objects as missing.‣nvidia-release-upgrade may report that not all updates have been installed and exit.When running the nvidia-release-upgrade command on DGX systems running DGX OS 4.99.x, it may exit and tell users: "Please install all available updates for your release before upgrading" even though all upgrades have been installed.Users who see this can run the following command:sudo apt install -y nvidia-fabricmanager-450/bionic-updates --allow-downgradesAfter running this, proceed with the regular upgrade steps:sudo apt updatesudo apt full-upgrade -ysudo apt install -y nvidia-release-upgradesudo nvidia-release-upgrade‣By default, Fabric Manager runs as a systemd service. If using DAEMONIZE=0 in the Fabric Manager configuration file, then the following steps may be required.1.Disable FM service from auto starting. (systemctl disable nvidia-fabricmanager)2.Once the system is booted, manually start FM process. (/usr/bin/nv-fabricmanager-c /usr/share/nvidia/nvswitch/fabricmanager.cfg). Note, since the processis not a daemon, the SSH/Shell prompt will not be returned (use another SSH shell for other activities or run FM as a background task).‣On NVSwitch systems with Windows Server 2019 in shared NVSwitch virtualization mode, the host may hang or crash when a GPU is disabled in the guest VM. This issue is under investigation.GPU Performance CountersThe use of developer tools from NVIDIA that access various performance countersrequires administrator privileges. See this note for more details. For example, reading NVLink utilization metrics from nvidia-smi (nvidia-smi nvlink -g 0) would require administrator privileges.NoScanout ModeNoScanout mode is no longer supported on NVIDIA Data Center GPU products. If NoScanout mode was previously used, then the following line in the “screen” section of /etc/X11/xorg.conf should be removed to ensure that X server starts on data center products:Option "UseDisplayDevice" "None"NVIDIA Data Center GPU products now support one display of up to 4K resolution.Unified Memory SupportSome Unified Memory APIs (for example, CPU page faults) are not supported on Windows in this version of the driver. Review the CUDA Programming Guide on the system requirements for Unified MemoryCUDA and unified memory is not supported when used with Linux power management states S3/S4.IMPU FRU for Volta GPUsThe driver does not support the IPMI FRU multi-record information structure for NVLink. See the Design Guide for Tesla P100 and Tesla V100-SXM2 for more information.OpenCL 3.0 Known IssuesDevice side enqueue‣Device-Side-Enqueue related queries may return 0 values, although corresponding built-ins can be safely used by kernel. This is in accordance with conformance requirements described at https:///registry/OpenCL/specs/3.0-unified/html/OpenCL_API.html#opencl-3.0-backwardscompatibility‣Shared virtual memory - the current implementation of shared virtual memory is limited to 64-bit platforms only.Chapter 2.VirtualizationTo make use of GPU passthrough with virtual machines running Windows and Linux, the hardware platform must support the following features:‣ A CPU with hardware-assisted instruction set virtualization: Intel VT-x or AMD-V.‣Platform support for I/O DMA remapping.‣On Intel platforms, the DMA remapper technology is called Intel VT-d.‣On AMD platforms, it is called AMD IOMMU.Support for these features varies by processor family, product, and system, and should be verified at the manufacturer's website.Supported HypervisorsThe following hypervisors are supported:Data Center products now support one display of up to 4K resolution.Supported Graphics CardsThe following GPUs are supported for device passthrough:VirtualizationChapter 3.Hardware and SoftwareSupportSupport for these features varies by processor family, product, and system, and should be verified at the manufacturer's website.Supported Operating Systems for NVIDIA Data Center GPUsThe Release 515 driver is supported on the following operating systems:‣Windows x86_64 operating systems:‣Microsoft Windows® Server 2022‣Microsoft Windows® Server 2019‣Microsoft Windows® Server 2016‣Microsoft Windows® 11‣Microsoft Windows® 10‣The following table summarizes the supported Linux 64-bit distributions. For a complete list of distributions, kernel versions supported, see the CUDA Linux System Requirements documentation.Note: This release was not tested with Rocky Linux 9.0Note: SUSE Linux Enterprise Server (SLES) 15.3 is provided as a preview for Arm64server since there are known issues when running some CUDA applications related todependencies on glibc 2.27.Supported Operating Systems and CPU Configurations for NVIDIA HGX A100 The Release 515 driver is validated with NVIDIA HGX A100 on the following operating systems and CPU configurations:‣Linux 64-bit distributions:‣Debian 11.3‣Red Hat Enterprise Linux 8.6 (in 4/8/16-GPU configurations)‣Red Hat Enterprise Linux 7.9 (in 4/8/16-GPU configurations)‣Rocky Linux 8.6 (in 4/8/16-GPU configurations)‣Red Hat Enterprise Linux 9.0 (in 4/8/16-GPU configurations)‣CentOS Linux 7.9 (in 4/8/16-GPU configurations)‣Ubuntu 18.04.6 LTS (in 4/8/16-GPU configurations)‣SUSE SLES 15.3 (in 4/8/16-GPU configurations)‣CPU Configurations:‣AMD Rome in PCIe Gen4 mode‣Intel Skylake/Cascade Lake (4-socket) in PCIe Gen3 modeSupported Virtualization ConfigurationsThe Release 515 driver is validated with HGX A100 on the following configurations:‣Passthrough (full visibility of GPUs and NVSwitches to guest VMs):‣8-GPU configurations with Ubuntu 18.04.6 LTS‣Shared NVSwitch (guest VMs only have visibility of GPUs and full NVLink bandwidth between GPUs in the same guest VM):‣16-GPU configurations with Ubuntu 18.04.6 LTSAPI SupportThis release supports the following APIs:‣NVIDIA® CUDA® 11.7 for NVIDIA® Maxwell TM, Pascal TM, Volta TM, Turing TM, and NVIDIA Ampere architecture GPUs‣OpenGL® 4.6‣Vulkan® 1.3‣DirectX 11‣DirectX 12 (Windows 10)‣Open Computing Language (OpenCL TM software) 3.0Note that for using graphics APIs on Windows (i.e. OpenGL, Vulkan, DirectX 11, and DirectX 12) or any WDDM 2.0+ based functionality on Data Center GPUs, vGPU is required. See the vGPU documentation for more information.Supported NVIDIA Data Center GPUsThe NVIDIA Data Center GPU driver package is designed for systems that have one ormore Data Center GPU products installed. This release of the driver supports CUDA C/C++ applications and libraries that rely on the CUDA C Runtime and/or CUDA Driver API. Attention: Release 470 was the last driver branch to support Data Center GPUs based on the Kepler architecture. This includes discontinued support for the following compute capabilities:‣sm_30 (Kepler)‣sm_32 (Kepler)‣sm_35 (Kepler)‣sm_37 (Kepler)For more information on GPU products and compute capability, see https:///cuda-gpus.NoticeThis document is provided for information purposes only and shall not be regarded as a warranty of a certain functionality, condition, or quality of a product. NVIDIA Corporation (“NVIDIA”) makes no representations or warranties, expressed or implied, as to the accuracy or completeness of the information contained in this document and assumes no responsibility for any errors contained herein. NVIDIA shall have no liability for the consequences or use of such information or for any infringement of patents or other rights of third parties that may result from its use. This document is not a commitment to develop, release, or deliver any Material (defined below), code, or functionality.NVIDIA reserves the right to make corrections, modifications, enhancements, improvements, and any other changes to this document, at any time without notice.Customer should obtain the latest relevant information before placing orders and should verify that such information is current and complete.NVIDIA products are sold subject to the NVIDIA standard terms and conditions of sale supplied at the time of order acknowledgement, unless otherwise agreed in an individual sales agreement signed by authorized representatives of NVIDIA and customer (“Terms of Sale”). NVIDIA hereby expressly objects to applying any customer general terms and conditions with regards to the purchase of the NVIDIA product referenced in this document. No contractual obligations are formed either directly or indirectly by this document.NVIDIA products are not designed, authorized, or warranted to be suitable for use in medical, military, aircraft, space, or life support equipment, nor in applications where failure or malfunction of the NVIDIA product can reasonably be expected to result in personal injury, death, or property or environmental damage. NVIDIA accepts no liability for inclusion and/or use of NVIDIA products in such equipment or applications and therefore such inclusion and/or use is at customer’s own risk.NVIDIA makes no representation or warranty that products based on this document will be suitable for any specified use. Testing of all parameters of each product is not necessarily performed by NVIDIA. It is customer’s sole responsibility to evaluate and determine the applicability of any information contained in this document, ensure the product is suitable and fit for the application planned by customer, and perform 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 document. NVIDIA accepts no 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 document or (ii) customer product designs.No license, either expressed or implied, is granted under any NVIDIA patent right, copyright, or other NVIDIA intellectual property right under this document. Information published by NVIDIA regarding third-party products or services does not constitute a license from NVIDIA to use such products or services or a warranty or endorsement thereof. Use of such information may require a license from a third party under the patents or other intellectual property rights of the third party, or a license from NVIDIA under the patents or other intellectual property rights of NVIDIA.Reproduction of information in this document is permissible only if approved in advance by NVIDIA in writing, reproduced without alteration and in full compliance with all applicable export laws and regulations, and accompanied by all associated conditions, limitations, and notices.THIS DOCUMENT AND ALL NVIDIA DESIGN SPECIFICATIONS, REFERENCE BOARDS, FILES, DRAWINGS, DIAGNOSTICS, LISTS, AND OTHER DOCUMENTS (TOGETHER AND SEPARATELY, “MATERIALS”) ARE BEING PROVIDED “AS IS.” NVIDIA MAKES NO WARRANTIES, EXPRESSED, IMPLIED, STATUTORY, OR OTHERWISE WITH RESPECT TO THE MATERIALS, AND EXPRESSLY DISCLAIMS ALL IMPLIED WARRANTIES OF NONINFRINGEMENT, MERCHANTABILITY, AND FITNESS FOR A PARTICULAR PURPOSE. TO THE EXTENT NOT PROHIBITED BY LAW, IN NO EVENT WILL NVIDIA BE LIABLE FOR ANY DAMAGES, INCLUDING WITHOUT LIMITATION ANY DIRECT, INDIRECT, SPECIAL, INCIDENTAL, PUNITIVE, OR CONSEQUENTIAL DAMAGES, HOWEVER CAUSED AND REGARDLESS OF THE THEORY OF LIABILITY, ARISING OUT OF ANY USE OF THIS DOCUMENT, EVEN IF NVIDIA HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. Notwithstanding any damages that customer might incur for any reason whatsoever, NVIDIA’s aggregate and cumulative liability towards customer for the products described herein shall be limited in accordance with the Terms of Sale for the product.TrademarksNVIDIA and the NVIDIA logo 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© 2022 NVIDIA Corporation & affiliates. All rights reserved.。