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hudi non global index 索引参数

hudi non global index 索引参数

hudi non global index 索引参数
Hudi非全局索引索引参数包括以下几个方面:
1. 范围查询键:这是进行范围查询的键。

例如,在时间戳上建
立非全局索引时,时间戳就是范围查询键。

使用这个键可以快速找到
满足一定时间范围的数据。

2. 前缀长度:这是非全局索引前缀的长度。

根据前缀长度建立
的索引可以提高查询的效率,但同时也会占用更多的存储空间。

因此,需要在效率和存储空间之间做出权衡。

3. 存储位置:非全局索引可以存储在不同的位置。

可以存储在HDFS、S3等等。

选择不同的存储位置会对索引的性能和可操作性产生
影响。

4. 索引类型:根据数据类型的不同,可以选择不同的索引类型,例如B-树、哈希表等。

不同的索引类型有着不同的优缺点,需要根据
实际情况选择。

总之,选择不同的非全局索引参数可以对索引的性能和存储空间
产生影响,需要根据实际情况进行权衡和选择。

NVIDIA NCCL 用户指南说明书

NVIDIA NCCL 用户指南说明书

Berkeley Software Distribution (BSD)TABLE OF CONTENTS Chapter 1. License (1)Copyright (c) 2015-2018, NVIDIA CORPORATION. All rights reserved. Redistribution and use in source and binary forms, with or without modification, arepermitted provided that the following conditions are met:‣Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.‣Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.‣Neither the name of NVIDIA CORPORATION, Lawrence Berkeley National Laboratory, the U.S. Department of Energy, nor the names of their contributors may be used to endorse or promote products derived from this software without specific prior written permission.THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FORA PARTICULAR PURPOSE ARE DISCLAIMED. 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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. 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Other company and product names may be trademarks of the respective companies with which they are associated.Copyright© 2018 NVIDIA Corporation. All rights reserved.。

distributeddataparallel参数

distributeddataparallel参数

distributeddataparallel参数distributeddataparallel是一个PyTorch库中的函数,用于在分布式环境中使用数据并行进行模型训练。

它的参数如下:- module (torch.nn.Module):要进行数据并行训练的模型。

- device_ids (List[int] or None, optional):用于训练的GPU设备的ID列表。

如果为None,则默认使用所有可用的GPU设备。

- output_device (int or None, optional):指定输出的设备ID。

如果为None,则默认使用第一个设备的ID。

- broadcast_buffers (bool, optional):指定是否在第一个设备上广播模型缓冲区。

默认为True。

- process_group (torch.distributed.ProcessGroup, optional):指定用于分布式训练的进程组。

如果未提供,则使用默认的分布式进程组。

- bucket_cap_mb (int, optional):每个网络参数块的预期最大容量(以MB为单位)。

默认为25MB。

- find_unused_parameters (bool, optional):指定是否查找未使用的参数。

默认为False。

- check_reduction (bool, optional):指定是否检查是否出现了梯度的减少。

默认为False。

- gradient_as_bucket_view (bool, optional):指定是否将梯度视为一个视图,以便可以在不同的设备上使用。

默认为False。

- device (torch.device, optional):指定模型和相关参数所在的设备。

默认为当前设备。

- broadcast_buffers回归以及等price等取缓冲区?这些参数可以根据具体的训练需求进行调整。

freemodbus enter_critical_section -回复

freemodbus enter_critical_section -回复

freemodbus enter_critical_section -回复"freemodbus enter_critical_section"是一个关于FreeRTOS中间件库Freemodbus的操作或函数。

FreeMODBUS是一个开源的Modbus协议栈实现,用于实现Modbus通信协议。

其中的enter_critical_section 函数用于进入临界区,用于保护关键代码段,防止多任务环境下发生竞争条件。

在一个多任务环境下,由于多个任务同时运行,可能会导致资源竞争的问题。

为了保证代码的正确性和可靠性,需要对关键代码段进行临界区保护。

Freemodbus提供了一个enter_critical_section函数,可以将关键代码段包裹在临界区中,确保其始终只能被一个任务执行。

下面将一步一步回答关于"freemodbus enter_critical_section"的问题,深入探讨如何正确使用和理解其中的细节。

1. 什么是临界区?临界区是指一个代码段,在该代码段被执行的过程中,不会被其他任务或中断中的代码插入执行。

通过使用临界区,可以避免多任务环境下的资源竞争问题,并且保证关键代码的正确执行。

2. 为什么需要临界区保护?在多任务环境下,当多个任务同时访问共享资源时,可能会出现竞争条件。

竞争条件是指多个任务尝试同时进行对共享资源的读或写操作,导致结果无法预测或不符合预期。

为了避免竞争条件,需要对共享资源的访问进行临界区保护。

3. enter_critical_section函数的作用是什么?enter_critical_section函数是Freemodbus库提供的函数之一,用于将一段关键代码包裹在临界区内。

通过调用enter_critical_section函数,可以确保关键代码只能被一个任务执行,从而避免竞争条件的发生。

4. enter_critical_section函数的使用方法是什么?在使用enter_critical_section函数时,需要按照以下步骤进行:a. 在关键代码段之前调用enter_critical_section函数,进入临界区。

slurm的原理

slurm的原理

slurm的原理Slurm是一种用于管理超级计算机集群的开源作业调度系统。

它的设计目标是在多用户、多任务的环境中高效地分配计算资源,以实现最佳的系统利用率和作业性能。

Slurm的核心原理是基于作业调度和资源管理。

它通过一个中央控制节点(controller)和多个计算节点(compute nodes)之间的协作,实现对作业的提交、调度和执行的管理。

在Slurm中,用户可以通过向控制节点提交作业描述文件来请求计算资源,包括指定需要的节点数量、运行时间、内存需求等。

控制节点根据预定义的调度策略和系统资源状况,将作业分配给计算节点进行执行。

Slurm的调度算法是其原理的核心部分。

它采用了先进的资源分配算法,如Backfilling和负载平衡算法,以最大程度地减少作业的等待时间和系统的负载不均衡。

Backfilling算法允许较短的作业在等待队列中插队执行,以便更好地利用系统资源。

负载平衡算法则根据节点的负载情况,动态地将作业分配给最适合的节点,以实现整个集群的负载均衡。

Slurm还具有高可用性和容错性的特性。

它支持多个控制节点的冗余配置,以防止单点故障导致的系统中断。

当一个控制节点失效时,其他节点会接管其功能,保证系统的持续运行。

此外,Slurm还提供了详细的日志记录和错误处理机制,以便管理员对系统进行监控和管理。

除了基本的作业调度和资源管理功能,Slurm还提供了丰富的扩展功能和插件机制。

用户可以通过自定义插件来扩展Slurm的功能,如添加新的调度策略、资源限制规则等。

这使得Slurm能够适应不同的应用场景和需求,满足各种复杂的计算任务的要求。

Slurm作为一种高效灵活的作业调度系统,通过合理的资源分配和调度算法,实现了对超级计算机集群的有效管理。

它的原理基于作业调度和资源管理,通过中央控制节点和计算节点的协作,实现作业的提交、调度和执行。

同时,Slurm还具有高可用性和容错性的特性,支持插件扩展,使其适用于各种复杂的计算任务。

自组织映射(SOM)R包说明书

自组织映射(SOM)R包说明书

Package‘som’October14,2022Version0.3-5.1Date2010-04-08Title Self-Organizing MapAuthor Jun Yan<***************.edu>Maintainer Jun Yan<***************.edu>Depends R(>=2.10)Description Self-Organizing Map(with application in gene clustering).License GPL(>=3)Repository CRANDate/Publication2016-07-0610:26:15NeedsCompilation yesR topics documented:filtering (1)normalize (2)plot.som (3)qerror (4)som (4)summary.som (7)yeast (7)Index9 filtering Filter data before feeding som algorithm for gene expression dataDescriptionFiltering data by certainfloor,ceiling,max/min ratio,and max-min difference.12normalizeUsagefiltering(x,lt=20,ut=16000,mmr=3,mmd=200)Argumentsx a data frame or matrix of input data.ltfloor value replaces those less than it with the valueut ceiling value replaced those greater than it with the valuemmr the max/min ratio,rows with max/min<mmr will be removedmmd the max-min difference,rows with(max-min)<mmd will be removed ValueAn dataframe or matrix after thefilteringAuthor(s)Jun Yan<***************.edu>See Alsonormalize.normalize normalize data before feeding som algorithmDescriptionNormalize the data so that each row has mean0and variance1.Usagenormalize(x,byrow=TRUE)Argumentsx a data frame or matrix of input data.byrow whether normalizing by row or by column,default is byrow.ValueAn dataframe or matrix after the normalizing.Author(s)Jun Yan<***************.edu>plot.som3See Alsofiltering.plot.som Visualizing a SOMDescriptionPlot the SOM in a2-dim map with means and sd bars.Usage##S3method for class somplot(x,sdbar=1,ylim=c(-3,3),color=TRUE,ntik=3,yadj=0.1,xlab="",ylab="",...)Argumentsx a som objectsdbar the length of sdbar in sd,no sdbar if sdbar=0ylim the range of y axies in each cell of the mapcolor whether or not use color plottingntik the number of tiks of the vertical axisyadj the proportion used to put the number of obsxlab x labelylab y label...other options to plotNoteThis function is not cleanly written.The original purpose was to mimic what GENECLUSTER does.The ylim is hardcoded so that only standardized data could be properly plotted.There are visualization methods like umat and sammon in SOM\_PAK3.1,but not implemented here.Author(s)Jun Yan<***************.edu>Examplesfoo<-som(matrix(rnorm(1000),250),3,5)plot(foo,ylim=c(-1,1))4som qerror quantization accuracyDescriptionget the average distortion measureUsageqerror(obj,err.radius=1)Argumentsobj a‘som’objecterr.radius radius used calculating qerrorValueAn average of the following quantity(weighted distance measure)over all x in the sample,||x−m i||h ciwhere h ci is the neighbourhood kernel for the ith code.Author(s)Jun Yan<***************.edu>Examplesfoo<-som(matrix(rnorm(1000),100),2,4)qerror(foo,3)som Function to train a Self-Organizing MapDescriptionProduces an object of class"som"which is a Self-Organizing Mapfit of the data.som5 Usagesom.init(data,xdim,ydim,init="linear")som(data,xdim,ydim,init="linear",alpha=NULL,alphaType="inverse",neigh="gaussian",topol="rect",radius=NULL,rlen=NULL,err.radius=1,inv.alp.c=NULL)som.train(data,code,xdim,ydim,alpha=NULL,alphaType="inverse",neigh="gaussian",topol="rect",radius=NULL,rlen=NULL,err.radius=1,inv.alp.c=NULL) som.update(obj,alpha=NULL,radius=NULL,rlen=NULL,err.radius=1,inv.alp.c=NULL)som.project(obj,newdat)Argumentsobj a‘som’object.newdat a new dataset needs to be projected onto the map.code a matrix of initial code vector in the map.data a data frame or matrix of input data.xdim an integer specifying the x-dimension of the map.ydim an integer specifying the y-dimension of the map.init a character string specifying the initializing method.The following are per-mitted:"sample"uses a radom sample from the data;"random"uses randomdraws from N(0,1);"linear"uses the linear grids upon thefirst two principlecomponents directin.alpha a vector of initial learning rate parameter for the two training phases.Decreaseslinearly to zero during training.alphaType a character string specifying learning rate funciton type.Possible choices arelinear function("linear")and inverse-time type function("inverse").neigh a character string specifying the neighborhood function type.The following arepermitted:"bubble""gaussian"topol a character string specifying the topology type when measuring distance in themap.The following are permitted:"hexa""rect"radius a vector of initial radius of the training area in som-algorithm for the two trainingphases.Decreases linearly to one during training.rlen a vector of running length(number of steps)in the two training phases.err.radius a numeric value specifying the radius when calculating average distortion mea-sure.inv.alp.c the constant C in the inverse learning rate function:alpha0*C/(C+t);6somValue‘som.init’initializes a map and returns the code matrix.‘som’does the two-step som training ina batch fashion and return a‘som’object.‘som.train’takes data,code,and traing parameters andperform the requested som training.‘som.update’takes a‘som’object and further train it with updated paramters.‘som.project’projects new data onto the map.An object of class"som"representing thefit,which is a list containing the following components: data the dataset on which som was applied.init a character string indicating the initializing method.xdim an integer specifying the x-dimension of the map.ydim an integer specifying the y-dimension of the map.code a metrix with nrow=xdim*ydim,each row corresponding to a code vector of a cell in the map.The mapping from cell coordinate(x,y)to the row index in thecode matrix is:rownumber=x+y*xdimvisual a data frame of three columns,with the same number of rows as in data:x and y are the coordinate of the corresponding observation in the map,and qerror is thequantization error computed as the squared distance(depends topol)betweenthe observation vector and its coding vector.alpha0a vector of initial learning rate parameter for the two training phases.alpha a character string specifying learning rate funciton type.neigh a character string specifying the neighborhood function type.topol a character string specifying the topology type when measuring distance in the map.radius0a vector of initial radius of the training area in som-algorithm for the two training phases.rlen a vector of running length in the two training phases.qerror a numeric value of average distortion measure.code.sum a dataframe summaries the number of observations in each map cell.Author(s)Jun Yan<***************.edu>ReferencesKohonen,Hynninen,Kangas,and Laaksonen(1995),SOM-PAK,the Self-Organizing Map Pro-gram Package(version3.1).http://www.cis.hut.fi/research/papers/som\_tr96.ps.ZExamplesdata(yeast)yeast<-yeast[,-c(1,11)]yeast.f<-filtering(yeast)yeast.f.n<-normalize(yeast.f)foo<-som(yeast.f.n,xdim=5,ydim=6)foo<-som(yeast.f.n,xdim=5,ydim=6,topol="hexa",neigh="gaussian")plot(foo)summary.som7 summary.som summarize a som objectDescriptionprint out the configuration parameters of a som objectUsage##S3method for class somsummary(object,...)##S3method for class somprint(x,...)Argumentsobject,x a‘som’object...nothing yetAuthor(s)Jun Yan<***************.edu>yeast yeast cell cycleDescriptionThe yeast data frame has6601rows and18columns,i.e.,6601genes,measured at18time points. Usagedata(yeast)FormatThis data frame contains the following columns:Gene a character vector of gene nameszero a numeric vectorten a numeric vectortwenty a numeric vectorthirty a numeric vectorfourty a numeric vector8yeastfifty a numeric vectorsixty a numeric vectorseventy a numeric vectoreighty a numeric vectorninety a numeric vectorhundred a numeric vectorone.ten a numeric vectorone.twenty a numeric vectorone.thirty a numeric vectorone.fourty a numeric vectorone.fifty a numeric vectorone.sixty a numeric vectorSourceReferencesTamayo et.al.(1999),Interpreting patterns of gene expression with self-organizing maps:Methods and application to hematopoietic differentiation,PNAS V96,pp2907-2912,March1999.Index∗arithqerror,4∗clustersom,4∗datasetsyeast,7∗hplotplot.som,3∗manipfiltering,1normalize,2∗printsummary.som,7filtering,1,3normalize,2,2plot.som,3print.som(summary.som),7qerror,4som,4summary.som,7yeast,79。

distribution channel

distribution channel

Distribution channel networks and management1.IntroductionThe common definition of distribution channel is that when the products move from the producer to the final consumer or industrial user, the direct or indirect way which go through in the process of transferring the ownership is the distribution channel.In china, Market power transfer from the large retailers to distributors, wholesale trade in China is rising rapidly, and a well-developed distribution system covering both urban and rural areas, are formed within the past 10 years. Unfortunately, this distribution system developed very unhealthy.Apart from the role of government, in a sense, the market order is due to the decision of behavior of enterprises and distributors. Enterprises do not have the ability to standardize the behavior of distributors. Distributors lacks the conscious and self-discipline, so the Chinese market disorder is not surprising.As a result, distribution channel in China need improvement and rational management. The mode of it require innovation.2.Distribution channel development in the foreign countryDistribution channel has captured foreign business man's sight in the last century. I will cite a case about Coca-Cola.The beverage market in the United States has a huge sales of $ 54 billion a year , 353 ml or 8 ounces of a soft drink for every men, women and children. At the end of the 20th century, the Coca-Cola Company's products accounted for 43% of the above total, but the company aims to surpass 50%. Coca-Cola hopes to throw away their biggest competitor Pepsi which occupied 31% market share.Coca-Cola is facing a mature market. The extremely usual method has been used again and again. They captured market share from competitors hands with the new products, competitive pricing, and large-scale promotional weapons . But, nowadays, the Coca-Cola Company will use a new method: to emphasize channels. Through providing more intensive supply of Coca-Cola, storage and marketing of Coca-Cola products , financial distributors and retailers are more active in promote their product . Channel strategy 's core is that I want customers, I want your shelves, and I want your customer's appetite, and I would also like every point of the potential growth of market share in soft drink market.To achieve this goal, the Coca-Cola Company reached close collaboration with its bottlers, wholesalers and retailers in order to jointly develop an intensive channel strategy. Coca-Cola sell its distribution agreemen to the universities, drug chains, and a large number of retail stores which may become its soft drinks retailers . At the same time, Coca-Cola also give some convenience store who give the best placement and exclude their competitors from outside a extra reward. Coca-Cola products will appear in each convenient store, including schools, the Church, clinics and the club. In short, Coca-Cola can be purchased in any place people want to buy soft drinks . This new channel strategy enhanced the depth and width of Coca-Cola. The scope of the strategy include 2,000,000 reserves Coca-Cola store industry, 450,000 restaurants and fast food outlets, as well as 1.4 million vending machines. In fact, Coca-Cola build up a intensive distribution channel strategy so as to provide broad market coverage. Ultimately, CEO imaged Coca-Cola's distribution channels as a tap of every consumer kitchen. However, cold water is not run out of the tap instead of lathery Coca-Cola!Coca-Cola is the world's most familiar and well-known brand. But they still focused on channels, Coca-Cola recognized that a great product, and even the world-famous products, succeeded only in the quality. In fact, if Coca-Cola products are not ready to meet the consumer needs of the tens of thousands around the world, then Coca-Cola is also unworthy of the name.It is no doubt that the distribution channels makes this high-level supply possible. Coca-Cola and other competitors, tens of thousands of products and services must bedelivered through the distribution channels. Only in this way, a large number of such products and services could be delivered to companies and organizations' hands. Distribution channels and personnel constitute a complex and dynamic system. But the ultimate consumer only see a lot of work less than they do. Consumers only see just a lot of final result like creating a new type of store, services, decide of the structure and operation of distribution channels. Distribution channels affect tens of thousands of consumers' life who are dependent on the pyramid structure supply of products and service.3.Distribution channel networks and management in china3.1 Development status of distribution channel in ChinaChinese enterprises in the development have gradually realized the importance of distribution channels, and distribution channels typically accounted for 15% -40% of the share of the prices of goods and services. This figure also reflects the potential to improve competitiveness and profitability of enterprises through improving distribution channels.Scientific design and ingenuity distribution channel management can often bring a higher return for the enterprise.The technology development is accelerating the evolution of the channels. The next few years, the channel management is facing challenges and opportunities with speed growth. Information networks have been able to make the product or service provider to skip the traditional distributors so as to deal directly with the end customers. In addition, logistics field appeared a large number of innovations, including the efficient and reliable overnight courier distributor inventory status and real-time tracking information systems, these innovations out of the original product and parts inventory system, and to create the conditions for the distribution channel network of recycling.At the same time, all walks of life appeared many new distribution channels, and create opportunities for businesses to cut costs and quickly occupy the specific market segments. For example, direct mail, the large warehouse supermarkets and online ordering. The importance of these new channels for consumer goods manufacturers isgrowing. Although there are such a wide range of important opportunities, few companies are able to take full advantage of it. Why enterprises' performance is not satisfactory in such a tactical significance field?3.2 The difficulties in seeking the new channel opportunitesPlenty of reasons cause channel opportunities difficult to identify.First, the shopping habits of consumers is not changed in one night,but in a gentle way. For instance, people accept credit cards and ATMs after a very long time. The large warehouse-style supermarkets have opened in many cities in China, this not only maintain the traditional distribution channels but also acquire the consumers' acceptance. Strangely, it did not attract the attention and interest of most manufacturers.Second, Chinese enterprises generally use external channels, rarely have direct contact with their end users, hopes distributors to discover and use new channels. In this mode, however, will inevitably lead to the exclusion of the distributors of emerging channels. Users of new channels often refuse to purchase from distributors, tend to deal directly with the enterprise.Finally, the biggest obstacle of channel innovation often exists in the internal enterprise. From management side, companies tend to focus on the control and management of distribution channels, neglect the importance of maintaining reasonable contact with consumers. They cannot understand the feelings and opinions of consumerstimely, comprehensive and accurately. Many companies can not even accurately grasp the consumers' buying habits.For an enterprises which has a hope to discover and use new channels,only a way to help them achieve their goals is to strengthen contact with the end-user and find buying habits from them. Enterprises must note that, even if the internal staff is their end users, their buying experience and habits is difficult on behalf of ordinary consumers, because the internal staff are often able to enjoy some privileges, they can not experience the ordinary consumer purchase feeling.4 How can we improve our distribution networks and managementTo solve the problem existing in distribution channels, almost all companies have indeed a very significant and positive action to take measures, but no roots, no innovation, so the action and measures is useless, which called positive inert in management. For example, many enterprises increase the clerk's practice, not only failed to reduce distribution costs but also resulted in a rise on the channel costs.Next, I will point out three major aspects of analysis methods to integrate distribution channels.4.1 Channel strategy and channel management4.1.1 Customer satisfaction should be considered as the main objective. The attention should be shifted from the distributors to the customer service.As long as customers meet their satisfaction, the enterprises can achieve good performance. This simple truth was overlooked by many businesses. Customer satisfaction determine customer loyalty. Customer loyalty will create a good premise of channel innovation for the enterprise and channel integration. Under this premise , the enterprise can focus on several lower cost for the customer but it can bring real benefitsof the things, in order to avoid or minimize the cost of those who do not attach importance to the target customer.Unfortunately , in many industries, the enterprise did not do so. They placed distributors demand above the customers', even neglect end-users' needs and end up hurting customer satisfaction, until lose the customer loyalty.4.1.2 To re-examine and develop channel strategy and strategicChannels should be determined by the customer demand and economic efficiency. We should be concerned about the operation of the channels (sales, distribution, telegraph and other) whether they are effective and prompt . The results and performance of the channels should also be evaluated from the angle of the main target customers .Channels constitution often has been a clear division of labor on the channel. And it determines which channels should serve the small quantities of high-profits products, which channels should adopt the principle of small profits with large quantities.For most businesses, a thorough study of the existing or potential channels and escaping from the shackles of a single channel, multi-channel strategy reasonable, is the primary means to increase market share and sales.When enterprises use the new channels, distributors and salesmen's direct reaction is to worry about a channel conflict that generate price competition and distribution fault problems. It needs to be emphasized that first of all,the price competition and distribution fault problems is the management issues, and then channel issyes. Distribution and sale are definitely two concepts. The distribution process should be carefully controlled, and can be fully involved in sales (retail) link. Every conflict has its solution. Do not break down the value of questionable channels because of the conflicts.4.2 Customer relationship managementThe true customer relationship management (CRM) was implement with large obstacles in China. Because even the common customer profiles are difficult to establish. Their customer profiles are rough, inaccurate and obsolete data. This is not the biggest problem. The biggest problem is not knowing how to use customer information for management and marketing services.4.2.1 Classification of the existing total dealer, different management approach to different categoriesEnterprises should divide them into available and unavailable according to their attitude and ability, eliminate the unavailable ones. Enterprises must get rid of the impact of emotional factors, and also do not worry about the possible short-term impact on the sales to the phase-out of a distributor. Enterprise need bigger and bigger scale , but it also need a better and healthy internal environment. A healthy distribution channels is the core to a healthy enterprise. This conclusion has been proved by the reality.The next step is the further classification. Available ones must be divided into training and reforming ones . Give the former ones free training, or eliminate them.4.2.2 The re-design and definition of the content and the role of the client filesThrough a comprehensive, systematic and professional management methods, a full range of management will implement on customers . Second, enterprise should expand the role of the client files into the market management tools and management tools . Finally, customer profiles about the total dealer should be extended to all distributors, to establish a comprehensive second installment and retailers file, and gradually amplitude gradually from distributors to consumers. The ultimate extension is to the end-users.4.2.3 The use of modern information technology to create and deal with customers, market information systemsMost enterprise customers and market information cannot be established. On the one hand, ignoring cause the result. More importantly, they do not know what information to collect , do not know how to deal with information. Not all information can be used, only the customer knowledge extracted from the complex information and market knowledge is useful, know only establish enterprise content management system, in order to make the customer knowledge and market knowledge business management and marketing services. The use of large-scale sales of businesses, if you do not use modern information technologies and means, to establish a sound and valuable information is almost unthinkable.4.3 ClerkUltimately, no matter what and how to do in channel innovation, clerk is the advanced people to promote and operate it. Even if the use of modern information technologies and means, it can not replace the personal skills of distributors and salesmen, and not a substitute for the simple and correct market insight.The success or failure of the enterprise channel innovation, largely depends on the clerks' correct understanding of the necessity and urgency of the innovation, the ability to enhance the personal skills required by the innovation.4.3.1 Professional training on the salesmanClerk of the Chinese enterprises, even those good enterprise, the professional clerk is also very limited. Many companies implement salesman training, but this training exists many problems: do not let the salesman to recognize the need for training. The salesman did not really enter the training status, or too utilitarian; no training systems, training can not be sustained, not ongoing training. It is difficult to play a role. Assessment of the effectiveness of training is not combined with personal income and advancement.4.3.2 Redefine the role of salesmanThe role of most businesses salesman in the individual marketing is the basis for the definition of the channel innovation. And it will be conducted in a professional marketing and system marketing base. The salesman's work is no longer based on the sales of the core, but the core of a solid marketing foundation work. The appraisal of the clerk firstly depend on the action points in the course of action,then the sales. Objectively speaking, simple training and education cannot solve the problem. The real difficulty is that this is a a specialization and professionalization process. Recognizing this, the enterprise just know how much effort and costs they will take in order to achieve their goals. The biggest difference of Chinese enterprises is that they only need to find the problems and solutions can be. And we should not only find a solution, it is but also more important to solve the specialization and professionalization of personnel.5.ConclusionThe importance of distribution channel is obvious in the 21 century. Distribution channel in China need improvement and rational management. The mode of it require innovation.6.ReferenceFeng,L.Y. 2002, Distribution channel management, Economic Management Press.。

distribution用法

distribution用法

distribution用法1. 什么是distribution在计算机领域,distribution指的是将软件、数据或资源分发给客户或用户的过程和方式。

它是将计算机程序、库文件、文档和其他相关文件打包成一个可执行的安装包或可分发的文件集合的过程。

2. distribution的种类2.1 操作系统发行版操作系统发行版(operating system distribution)是指将一个操作系统的内核、驱动程序、应用程序和工具集成在一起,并将其分发给用户使用的操作系统软件包。

常见的操作系统发行版包括Linux发行版(如Ubuntu、Debian、Fedora)和UNIX发行版(如Solaris、FreeBSD)等。

这些发行版通常会包含一个安装程序,用户可以通过把发行版从光盘、网络或其他存储媒介上安装到计算机上,从而获取到一个完整的操作系统环境。

2.2 软件发行版软件发行版(software distribution)是指将软件、库文件和相关资源以一个整体的形式打包,并分发给用户使用的过程。

常见的软件发行版包括Python的pip包管理器、Java的jar文件和C/C++的二进制发行版等。

软件发行版通常包括了软件的可执行文件、配置文件、库文件、文档和示例代码等。

用户可以通过下载发行版的包来安装和使用软件。

2.3 数据分发数据分发(data distribution)是指将数据集合分发给用户使用的过程。

数据分发可以包括数据的复制、数据集合的分割和数据访问的授权等。

常见的数据分发方式包括数据库的复制、分布式文件系统和P2P网络等。

数据分发可以提高数据的可靠性和可用性,同时减轻数据访问的负载。

例如,在分布式系统中,通过将数据复制到不同的节点上,可以提高数据的可靠性和访问速度。

3. distribution的作用3.1 提供便利的软件安装方式通过软件发行版,用户可以方便地安装和使用软件。

发行版通常包含了软件的所有依赖项和配置文件,用户只需要下载和运行发行版的安装程序,即可快速搭建一个可用的软件环境。

distributeddataparallel 随机数

distributeddataparallel 随机数

distributeddataparallel 随机数DistributedDataParallel(DDP)是一种用于分布式深度学习的技术,它可以在多个GPU或多台计算机上并行训练模型。

而随机数则是深度学习中非常重要的一个概念,它可以用于初始化模型参数、数据增强等方面。

在DDP中,随机数的生成也需要特别注意。

首先,我们来了解一下DDP的基本原理。

DDP使用了数据并行的思想,将数据分成多份,分别在不同的GPU或计算机上进行计算,最后将结果汇总。

在DDP中,每个进程都有自己的模型副本和数据子集,每个进程都会计算梯度并将其发送给其他进程,其他进程会将这些梯度相加并更新自己的模型副本。

这种方式可以大大加速模型训练,提高效率。

在DDP中,随机数的生成需要特别注意。

由于每个进程都有自己的模型副本和数据子集,因此每个进程都需要生成自己的随机数。

如果每个进程都使用相同的随机数种子,那么每个进程生成的随机数序列都是一样的,这会导致每个进程计算出的梯度也是一样的,最终的结果也会一样。

因此,每个进程都需要使用不同的随机数种子来生成随机数,以保证每个进程生成的随机数序列都是不同的。

在PyTorch中,可以使用torch.initial_seed()函数来获取当前进程的随机数种子。

可以通过设置torch.manual_seed()函数来设置随机数种子,以保证每个进程生成的随机数序列都是不同的。

在DDP中,可以使用torch.distributed.get_rank()函数来获取当前进程的ID,然后将其作为随机数种子的一部分,以保证每个进程生成的随机数序列都是不同的。

总之,在DDP中,随机数的生成需要特别注意,每个进程都需要使用不同的随机数种子来生成随机数,以保证每个进程生成的随机数序列都是不同的。

这样可以避免每个进程计算出的梯度都是一样的,最终的结果也会一样的问题。

slurm用法

slurm用法

Slurm用法1. 什么是Slurm?Slurm是一个开源的、高度可扩展的作业调度系统,用于在大型计算集群上管理和调度作业。

它是一个用于Linux环境的作业调度器,可以管理并分配计算资源,使得用户可以有效地利用集群资源进行计算任务。

2. Slurm的基本概念2.1 集群集群是由多个计算节点组成的计算环境。

每个计算节点都具有一定的计算资源,如CPU、内存、存储等。

Slurm可以管理和调度集群中的计算节点,根据作业的需求分配合适的计算资源。

2.2 作业作业是用户提交给Slurm的计算任务。

作业可以是一个单独的可执行程序,也可以是一个脚本。

用户可以指定作业的资源需求、运行时间限制等参数。

2.3 队列队列是Slurm中用于管理作业的概念。

Slurm将作业按照一定的规则分配到不同的队列中,然后按照队列的优先级和策略来调度作业的运行。

2.4 分区分区是Slurm中用于划分集群资源的概念。

一个集群可以被划分为多个不同的分区,每个分区可以有不同的计算节点和资源配额。

通过将集群划分为多个分区,可以更好地管理和调度不同类型的作业。

3. Slurm的安装和配置3.1 安装Slurm要安装Slurm,首先需要下载Slurm的源代码。

然后按照官方文档提供的步骤进行编译和安装。

安装完成后,需要在集群的每个计算节点上进行相应的配置。

3.2 配置SlurmSlurm的配置文件是slurm.conf,可以通过编辑该文件来配置Slurm的各种参数。

配置文件中包含了集群的基本信息、分区的配置、队列的配置等。

可以根据实际需求来修改配置文件。

4. Slurm的使用4.1 提交作业要提交一个作业,可以使用sbatch命令。

sbatch命令可以指定作业的资源需求、运行时间限制等参数。

例如:sbatch --partition=normal --nodes=2 --ntasks-per-node=4 --time=1:00:00 myjob.s h上述命令将提交一个作业,要求分配2个计算节点,每个节点上运行4个任务,运行时间限制为1小时。

greenplum分布策略

greenplum分布策略

Greenplum数据库是一个大规模并行处理(MPP)数据库,支持将数据在多个节点上并行存储和处理。

为了实现高效的数据分布和查询性能,Greenplum使用了分布策略来决定如何存储和分片数据。

在Greenplum中,可以使用以下几种分布策略:1. 随机分布(Random Distribution):将数据随机分布在所有的节点上,不考虑数据的特性。

2. 哈希分布(Hash Distribution):根据一个或多个列上的哈希值,将数据均匀地分布在各个节点上。

这种分布策略通常用于关联查询和连接操作。

3. 范围分布(Range Distribution):根据一个或多个列上的排序值,将数据按照一定的范围划分并分布在各个节点上。

这种分布策略通常用于范围查询和区间分析。

4. 复制分布(Replicated Distribution):将整个数据复制到每个节点上,用于频繁进行全局聚合查询或小型表的连接操作。

这种分布策略可以提供并行查询和高吞吐量。

选择合适的分布策略需要根据数据的特性、查询类型以及性能要求来决定。

通常,哈希分布适用于均衡地分布数据和支持连接操作,范围分布适用于范围查询,复制分布适用于小型维表和频繁的全局聚合查询。

在创建表和分区表时,可以使用Greenplum提供的语法指定分布策略。

例如,在创建表时可以使用如下语句来指定哈希分布:```sqlCREATE TABLE tablename (col1 datatype, col2 datatype, ...)DISTRIBUTED BY (col1);```或者使用以下语句来指定范围分布:```sqlCREATE TABLE tablename (col1 datatype, col2 datatype, ...)DISTRIBUTED BY RANGE (col1);```需要根据具体的数据和查询需求选择合适的分布策略,以达到最佳的查询性能和数据分布效果。

distribution的用法及短语

distribution的用法及短语

distribution的用法及短语
嘿,咱今儿就来唠唠“distribution”这个词儿!“distribution”常见的意
思就是“分配”“分布”呀。

比如说,财富的分配,那就是“the distribution
of wealth”。

就好像把一堆糖果分给一群小朋友,这就是一种distribution 嘛!
再来讲讲它的短语。

“distribution center”,这就是“配送中心”的意思呀,你想想,那不就是货物被分配到各个地方去的中心嘛。

还有“distribution network”,“分销网络”呀,就像一张大网,把东西四散开来。

咱说个例子哈,公司要把新生产的产品送到各个地区,那不得通过
配送中心嘛,这配送中心就是 distribution center 呀!这不就把这个短语用上啦。

然后呢,咱生活中也到处都有 distribution 的影子呀。

你看那阳光,
均匀地分布在大地上,这也是一种 distribution 呢!还有人口的分布,
有的地方人多,有的地方人少,这就是人口的 distribution 呀!
咱再想想,水在自然界的分布也是很有讲究的呀,有的地方水资源
丰富,有的地方就很缺水,这就是水的 distribution 不同嘛。

哎呀,说了这么多,你是不是对“distribution”的用法和短语更清楚啦?反正我觉得呀,理解了这个词,对咱生活和学习都挺有帮助的呢!
我的观点就是:“distribution”虽然是个简单的词,但它的用法和相
关短语在很多方面都很重要,能帮我们更好地理解和描述周围的世界。

free -h参数详解

free -h参数详解

free -h参数详解
在Linux系统中,free -h命令用于显示系统内存的使用情况。

-h参数是用于以人类可读的格式显示内存使用情况,以便更容易阅读和理解。

下面是free -h命令的参数详解:
bash
free -h
-h: 显示内存使用情况以人类可读的格式显示,单位为KB、MB、GB 等。

输出结果包含以下几部分:
total: 系统总的内存大小,以人类可读的格式显示。

used: 已使用的内存大小,以人类可读的格式显示。

free: 未使用的内存大小,以人类可读的格式显示。

shared: 被多个进程共享的内存大小。

buffers: 被缓冲区使用的内存大小。

cache: 被缓存使用的内存大小。

available: 可供新进程使用的内存大小。

freetds openssl 编译

freetds openssl 编译

一、背景介绍在编程开发中,有时候我们需要使用一些特定的库或工具来支撑我们的项目。

而在这些库或工具中,freetds和openssl可以说是比较常用的两个。

freetds是一个用来支持Mircosoft SQL Server和Sybase数据库的库,而openssl则是一个开源的加密工具包,被广泛用于网络安全通信方面。

在一些项目中,我们可能需要将这两个工具编译到我们的项目中,以实现特定的功能。

二、freetds编译1. 下载freetds源码我们需要到freetds的冠方全球信息站或者其他可靠的软件站点上下载freetds的源代码。

通常freetds的源码会以.tar.gz或者.zip的形式发布,我们需要下载并解压这个文件。

2. 配置编译参数在解压之后的文件夹中,我们可以找到一个configure文件,这个文件是用来配置freetds的编译参数的。

我们可以使用一些特定的参数来指定编译的选项,比如安装路径、是否启用SSL支持等等。

配置完成后,我们可以执行./configure命令来开始配置编译环境。

3. 编译当配置完成之后,我们可以使用make命令来进行编译。

make会根据之前的配置参数来编译freetds,并生成对应的库文件和可执行文件。

4. 安装编译完成后,我们可以使用make install命令来安装freetds。

这个命令会将编译生成的文件复制到指定的安装路径中,并且配置系统,使得我们可以在项目中使用freetds。

三、openssl编译1. 下载openssl源码和freetds一样,我们需要到openssl的冠方全球信息站或者其他可靠的软件站点上下载openssl的源代码。

通常openssl的源码也会以.tar.gz或者.zip的形式发布,我们需要下载并解压这个文件。

2. 配置编译参数在解压之后的文件夹中,我们可以找到一个config文件,这个文件是用来配置openssl的编译参数的。

和freetds一样,我们可以使用一些特定的参数来指定编译的选项,比如安装路径、是否启用某些加密算法等等。

hologres 分区表创建语句

hologres 分区表创建语句

在谈论Hologres分区表创建语句之前,我们需要先了解Hologres是什么以及为什么会需要分区表。

Hologres是一种高性能、高可用、分布式结构化数据查询和分析服务,它以分布式数据库的形式为用户提供了海量数据的存储和查询。

在处理大数据的场景下,为了提高查询效率和降低存储成本,通常需要对数据进行分区存储和查询。

Hologres分区表创建语句就显得至关重要。

在这篇文章中,我们将从简到繁地介绍Hologres分区表的创建语句,帮助读者更深入地理解分布式数据库分区表的概念和实现。

通过本文的阅读,读者将能够全面、深刻和灵活地理解Hologres分区表创建语句。

让我们来了解Hologres分区表的基本概念。

分区表是指将表中的数据根据特定的规则分布到不同的物理存储位置上,以便更快地查询和管理数据。

在Hologres中,通过创建分区表,可以使查询按照分区键进行过滤,从而加快查询速度。

这种设计不仅提高了数据查询的效率,还可以根据业务需求动态地增加或删除分区,实现了数据的灵活管理。

接下来,让我们针对Hologres分区表创建语句的具体实现进行讨论。

我们需要使用CREATE TABLE语句创建普通表,然后使用ALTER TABLE语句添加分区。

在Hologres中,分区表的创建语句主要涉及到PARTITION BY子句,用于指定分区键和分区类型。

我们可以按照时间范围、地区或者其他业务需求进行分区。

在创建分区表时,还需要设置分区的数量和规则,以及指定每个分区的存储位置和数据存储格式。

在实际应用中,根据具体的业务需求和数据特点,我们可以选择不同的分区表创建语句。

如果需要按照时间范围对数据进行查询,可以采用按时间范围分区的方式;如果需要根据地域信息进行查询,可以采用按地域分区的方式。

在选择Hologres分区表创建语句时,需要根据具体情况进行灵活应用,以提高数据查询效率和降低存储成本。

总结回顾,Hologres分区表的创建语句是分布式数据库中非常重要的一部分。

python feemd原理

python feemd原理

Python Feemd原理Python Feemd,全称为Python Finite Element Enriched Meshfree Method,是一种基于Python语言的有限元增强无网格方法。

它将有限元法和无网格方法相结合,能够对复杂的工程问题进行高效准确的数值求解。

本文将介绍Python Feemd的原理及其应用。

一、Python Feemd的基本原理1. 有限元法(Finite Element Method,FEM)是一种数值分析方法,用于求解工程和物理问题。

它将连续的物理模型离散化为有限数量的单元,再通过单元之间的相互作用来近似求解原始模型。

有限元法的核心是建立单元之间的插值函数和加权残值法求解方程。

2. 无网格方法(Meshfree Method)是一种不依赖于网格的数值解法,适用于具有复杂几何形状和大变形的问题。

该方法通过使用局部的无网格形式函数来描述计算区域的几何和物理特性,从而避免了网格生成和更新的复杂性。

3. Python Feemd将有限元法和无网格方法相结合,通过有限元法的单元插值和加权残值法,以及无网格方法的局部无网格形式函数,实现对复杂问题的高效求解。

它主要包括以下几个主要步骤:3.1 创建计算区域的离散化网格,将其划分为有限数量的单元。

3.2 定义单元内的插值函数,用于近似描述单元内的物理场。

3.3 通过加权残值法建立单元之间的相互作用,构建整个计算区域的线性方程组。

3.4 使用局部的无网格形式函数对计算区域的几何和物理特性进行描述,从而避免了网格更新和复杂性。

二、Python Feemd的应用1. Python Feemd可应用于复杂的工程问题求解,如结构力学、流体力学、热传导等。

它具有以下几个优点:1.1 高效准确:有限元法和无网格方法相结合,能够对复杂问题进行高效准确的数值求解。

1.2 适用性广泛:适用于具有复杂几何形状和大变形的问题,能够有效地处理非线性、动态、多物理场耦合等问题。

subdistribution 子分布 定义

subdistribution 子分布 定义

subdistribution 子分布定义
子分布,也称为条件分布或给定某一事件下的分布,是统计学中的一个重要概念。

当我们已知某一事件发生时,关于另一个事件的概率分布就是子分布。

在概率论中,子分布提供了给定某一事件条件下的随机变量的概率分布。

它可以帮助我们更好地理解变量之间的关系,以及在特定条件下的可能性。

子分布可以用条件概率的方式来表示。

对于两个随机变量X和Y,它们之间的子分布可以表示为P(Y|X)。

这表示在给定X的条件下,事件Y发生的概率。

子分布在统计学和机器学习中有广泛的应用。

例如,在数据分析中,我们经常需要根据给定的条件来推断某一事件的概率。

子分布可以帮助我们更准确地估计未知变量的概率分布,从而做出更精确的预测。

子分布还可以帮助我们理解多个随机变量之间的依赖关系。

通过分析子分布,我们可以研究变量之间的相关性以及它们对彼此的影响。

这对于探索数据背后的模式和规律非常重要。

子分布是条件概率分布的一种表达方式,它描述了在给定某一事件发生时,另一个事件的概率分布。

通过分析子分布,我们能够推断未知变量的概率,研究变量之间的依赖关系,并做出更准确的预测。

nn和2nn工作机制

nn和2nn工作机制

nn和2nn工作机制
NN 和 2NN 工作机制中,NN 通常指 NameNode,2NN 指 SecondaryNameNode。

下面以Hadoop 生态系统中的 HDFS(Hadoop Distributed File System)为例,对两者的工作机制进行简单说明:
1. NameNode 工作机制:
- 加载编辑日志(edits)和镜像文件(fsimage)到内存。

- 处理元数据的请求,如:增删改。

- 记录操作日志、更新滚动日志(避免发生意外,如:断电)。

- 内存数据操作,如:增删改。

2. SecondaryNameNode 工作机制:
- 请求是否需要 CheckPoint,触发 CheckPoint 需要满足两个条件中的任意一个,定时时间到和 Edits 中数据写满了。

- 请求执行 CheckPoint。

- 滚动正在写的 Edits(会生成一个新的日志,如果有新的元数据请求,那么就会用这个新的日志)。

- 将镜像文件和日志拷贝到2NN中。

- 加载到内存并合并。

- 生成新的镜像文件。

- 将新的镜像文件拷贝到 NN。

- NN 中覆盖历史的镜像文件。

在实际应用中,NN 和 2NN 的工作机制可能会因为不同的系统和需求而有所不同。

如果你需要更详细的信息,请提供更多上下文信息。

hologres原理

hologres原理

hologres原理Hologres原理Hologres是一种分析型云原生数据仓库,具有高性能、高并发、低延迟的特点。

它是由阿里云推出的一项云服务,旨在帮助企业更好地处理和分析海量数据。

Hologres的原理基于分布式计算和存储,采用了一系列先进的技术手段来实现其高效的数据处理能力。

一、分布式存储Hologres采用了分布式存储的方式来存储数据。

它将数据划分为多个分片,并将这些分片存储在不同的节点上。

这样可以将数据分散存储,提高数据的读写速度和并发处理能力。

同时,Hologres还采用了冷热分离的存储策略,将热数据存储在高速存储介质上,而将冷数据存储在低成本存储介质上,以降低存储成本。

二、分布式计算除了分布式存储外,Hologres还采用了分布式计算的方式来处理数据。

它将查询任务划分为多个子任务,并将这些子任务分配给不同的节点进行并行计算。

这样可以充分利用集群中的计算资源,提高查询的并发能力和响应速度。

同时,Hologres还支持动态扩展计算资源,可以根据实际的查询负载情况来调整集群的规模,以适应不同的业务需求。

三、数据索引为了提高查询效率,Hologres采用了多种数据索引技术。

它支持对数据表中的某些列进行索引,以加速查询操作。

在索引的帮助下,Hologres可以快速定位到符合查询条件的数据,避免全表扫描,提高查询的效率。

同时,Hologres还支持多种索引类型,例如B+树索引、哈希索引等,以适应不同类型的查询需求。

四、数据压缩为了减少存储空间的占用和提高数据的传输效率,Hologres采用了数据压缩技术。

它可以对数据进行压缩,减少数据在存储介质上的占用空间。

在数据传输过程中,Hologres还可以对数据进行压缩和解压缩,减少数据的传输量,提高数据的传输速度。

同时,Hologres还支持多种压缩算法,例如Snappy、LZ4等,以适应不同的数据压缩需求。

五、数据一致性为了保证数据的一致性和可靠性,Hologres采用了多种数据同步和备份策略。

torch distributions multinomial用法

torch distributions multinomial用法

在Pytorch中,多项分布Multinomial()是torch.distributions.multinomial中的一个类,接受四个参数:
1.total_count=1:接受int参数,指的是单次抽样的样本数量。

2.probs=None:接受Tensor参数,指的是各事件发生的概率或频数(当传入频数时可以
通过probs属性查看对应的概率分布)。

3.logits=None:接受Tensor参数,指的是各事件发生的概率的自然对数或频数(当传入
频数时可以通过logits属性查看对应的对数概率分布)。

4.validate_args=None:用于指定是否检查参数的合法性。

使用Multinomial类时,可以调用sample()函数进行抽样,该函数接收一个参数sample_shape=torch.Size()以指定抽样次数和输出张量的形状。

请注意,probs参数必须是非负、有限且具有非零总和,并且沿最后一个维度将其归一化为总和为1。

probs将返回此标准化值。

logits参数将被解释为非标准化对数概率,因此可以是任何实数。

它同样会被标准化,以便沿最后一个维度得到的概率总和为1。

logits将返回此标准化值。

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Free n-distributions: holonomy, sub-Riemannian structures, Fefferman constructions and dual distributions.
arXiv:0706.4441v1 [math.DG] 29 Jun 2007
Stuart Armstrong 2007
CONTENTS
1. Introduction
1Introຫໍສະໝຸດ uctionOn a manifold M , let H ⊂ T M be a distribution of rank n. Then there is a well defined map L : H ∧ H → T M/H . For X, Y sections of H , it is given by the quotiented Lie bracket X ∧ Y → [X, Y ]/H . Then H is a free n-distribution if L is an isomorphism. The moniker “free” comes from the fact that there are no relations between sections of H that would cause L to fail injectivity. This condition immediately implies that T M/H is of rank n(n − 1)/2, thus that M is of dimension m = n(n + 1)/2. Bryant [Bry05] has studied the case of n = 3, m = 3, a free 3-distribution in a 6-manifold, but the general case remains little studied. Fortunately, these structures lead themselves to be treated with the general tools of Cartan connecˇ ˇ tions on parabolic geometries ([CG02] and [CS00]). The homogeneous model is provided by the set n+1,n of maximal isotropic planes in R . The group of transformations is G = SO(n + 1, n) while the stabiliser of a point is P = GL(n) ⋊ Rn ⋊ ∧2 Rn . Its Lie algebra is p which has nilradical Rn ⋊ ∧2 Rn . These are precisely the two-step free nilpotent Lie algebras, with the Lie bracket from Rn ⊗ Rn to ∧2 Rn being given by taking the wedge. The fact this nil-radical is free is a consequence of the freeness of the n-distribution. We do not introduce any extra information, or make any choices by taking the Cartan connection, ˇ as the normal Cartan connection for a free n-distribution is determined entirely by H ([Cap06]). The most natural restrictions to put on the holonomy of a connection with structure algebra so(n + 1, n) is to require that it preserves a subundle in the natural representation bundle of that algebra – the standard Tractor bundle T . This condition is analysed; it turns out it implies a class of preferred connections on M , which preserve certain structures on the manifold, making them an example of sub-Riemannian manifolds. If the rank of the preserved bundle V ⊂ T is n, there is a unique preferred connection ∇ defined by it, that has properties analogous to the Einstein condition in conformal and projective geometry. If V is further non-degenerate, there is a well-defined metric on the manifold as a whole. In that case, it is an Einstein involution [Arm07]. Other issues worth looking into in any new geometries is how the structures restrict to sub-manifolds; this is analysed in the next section. There is even a decomposition/twisted product result, similar to the Einstein product result in conformal geometry ([Lei05] and [Arm05]) which applies to certain very restrictive holonomy algebras. In this case, there are explicit constructions of manifolds with these properties, leaving hope that manifolds with the weaker properties mentioned above will also exist. The main results of this paper will be gleaned in the n = 3, m = 6 case. The free 3-distribution has a Fefferman construction into the conformal structure [Bry05]. We will show this Fefferman construction is normal for both Tractor connections, meaning that we have many known examples of holonomy reductions [Arm05]. Here, the normal Cartan connection is torsion-free, and the results of the previous section can be applied to show that holonomy reductions to SU (2, 2) ∼ = Spin(4, 2)0 and SL(4, R) ∼ = Spin(3, 3)0 do exist, and arise from their own Fefferman constructions – over integrable CR manifolds and integrable Lagrangian contact structures, respectively. Here, normality of the underlying Cartan connections is equivalent with normality of that generated by the free 3-distribution. The other interesting situation for a free 3-distribution is that of a reduction of the holonomy of − → the Tractor connection ∇ to G′ 2 . This does not arise from any Fefferman construction, but has a fascinating geometry. On an open dense set of the manifold, there is a canonical Weyl structure ∇. This determines a splitting of T = T−2 ⊕ H , where H is the canonical free 3-distribution. 2
Abstract This paper analyses the parabolic geometries generated by a free n-distribution in the tangent space of a manifold. It shows that certain holonomy reductions of the associated normal Tractor connections, imply preferred connections with special properties, along with Riemannian or subRiemannian structures on the manifold. It constructs examples of these holonomy reductions in the simplest cases. The main results, however, lie in the free 3-distributions. In these cases, there are normal Fefferman constructions over CR and Lagrangian contact structures corresponding to holonomy reductions to SO(4, 2) and SO(3, 3), respectively. There is also a fascinating construction of a ‘dual’ distribution when the holonomy reduces to G′ 2.
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