With huge amounts of training data, deep learning has made great breakthroughs in many artificial intelligence (AI) applications. However, such large-scale data sets present computational challenges, requiring training to be distributed on a cluster equipped with accelerators like GPUs. With the fast increase of GPU computing power, the data communications among GPUs have become a potential bottleneck on the overall training performance. In this paper, we first propose a general directed acyclic graph (DAG) model to describe the distributed synchronous stochastic gradient descent (S-SGD) algorithm, which has been widely used in distributed deep learning frameworks. To understand the practical impact of data communications on training performance, we conduct extensive empirical studies on four state-of-the-art distributed deep learning frameworks (i.e., Caffe-MPI, CNTK, MXNet and TensorFlow) over multi-GPU and multi-node environments with different data communication techniques, including PCIe, NVLink, 10GbE, and InfiniBand. Through both analytical and experimental studies, we identify the potential bottlenecks and overheads that could be further optimized. At last, we make the data set of our experimental traces publicly available, which could be used to support simulation-based studies.
Decentralized methods implement the gradients aggregation by using the reduction tree (RT) or ring based all-reduce @cite_28 @cite_20 @cite_15 . The gradients are exchanged via MPI-like collectives (e.g., all-reduce). Very recently, some new collective communications libraries like Gloo https: github.com facebookincubator gloo and NCCL2 https: developer.nvidia.com nccl have been developed to support efficient communications among a set of GPUs. A. @cite_15 @cite_13 propose a high performance CUDA-Aware MPI to reduce the overhead of data communications across a GPU cluster. have shown that the optimized all-reduce implementation and the pipeline of all-reduce operations with gradient computation can lead to very good scalability @cite_29 .
@cite_28 @cite_29 @cite_15 @cite_13 @cite_20
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Dense Multi-GPU systems have recently gained a lot of attention in the HPC arena. Traditionally, MPI runtimes have been primarily designed for clusters with a large number of nodes. However, with the advent of MPI+CUDA applications and CUDA-Aware MPI runtimes like MVAPICH2 and OpenMPI, it has become important to address efficient communication schemes for such dense Multi-GPU nodes. This coupled with new application workloads brought forward by Deep Learning frameworks like Caffe and Microsoft CNTK pose additional design constraints due to very large message communication of GPU buffers during the training phase. In this context, special-purpose libraries like NVIDIA NCCL have been proposed for GPU-based collective communication on dense GPU systems. In this paper, we propose a pipelined chain (ring) design for the MPI_Bcast collective operation along with an enhanced collective tuning framework in MVAPICH2-GDR that enables efficient intra- inter-node multi-GPU communication. We present an in-depth performance landscape for the proposed MPI_Bcast schemes along with a comparative analysis of NVIDIA NCCL Broadcast and NCCL-based MPI_Bcast. The proposed designs for MVAPICH2-GDR enable up to 14X and 16.6X improvement, compared to NCCL-based solutions, for intra- and inter-node broadcast latency, respectively. In addition, the proposed designs provide up to 7 improvement over NCCL-based solutions for data parallel training of the VGG network on 128 GPUs using Microsoft CNTK. Traditionally, MPI runtimes have been designed for clusters with a large number of nodes. However, with the advent of MPI+CUDA applications and dense multi-GPU systems, it has become important to design efficient communication schemes. This coupled with new application workloads brought forward by Deep Learning frameworks like Caffe and Microsoft CNTK pose additional design constraints due to very large message communication of GPU buffers during the training phase. In this context, special-purpose libraries like NCCL have been proposed. In this paper, we propose a pipelined chain (ring) design for the MPI_Bcast collective operation along with an enhanced collective tuning framework in MVAPICH2-GDR that enables efficient intra- internode multi-GPU communication. We present an in-depth performance landscape for the proposed MPI_Bcast schemes along with a comparative analysis of NCCL Broadcast and NCCL-based MPI_Bcast. The proposed designs for MVAPICH2-GDR enable up to 14X and 16.6X improvement, compared to NCCL-based solutions, for intra- and internode broadcast latency, respectively. In addition, the proposed designs provide up to 7 improvement over NCCL-based solutions for data parallel training of the VGG network on 128 GPUs using Microsoft CNTK. The proposed solutions outperform the recently introduced NCCL2 library for small and medium message sizes and offer comparable better performance for very large message sizes. Emerging paradigms like High Performance Data Analytics (HPDA) and Deep Learning (DL) pose at least two new design challenges for existing MPI runtimes. First, these paradigms require an efficient support for communicating unusually large messages across processes. And second, the communication buffers used by HPDA applications and DL frameworks generally reside on a GPU's memory. In this context, we observe that conventional MPI runtimes have been optimized over decades to achieve lowest possible communication latency for relatively smaller message sizes (up-to 1 Megabyte) and that too for CPU memory buffers. With the advent of CUDA-Aware MPI runtimes, a lot of research has been conducted to improve performance of GPU buffer based communication. However, little exists in current state of the art that deals with very large message communication of GPU buffers. In this paper, we investigate these new challenges by analyzing the performance bottlenecks in existing CUDA-Aware MPI runtimes like MVAPICH2-GDR, and propose hierarchical collective designs to improve communication latency of the MPI_Bcast primitive by exploiting a new communication library called NCCL. To the best of our knowledge, this is the first work that addresses these new requirements where GPU buffers are used for communication with message sizes surpassing hundreds of megabytes. We highlight the design challenges for our work along with the details of design and implementation. In addition, we provide a comprehensive performance evaluation using a Micro-benchmark and a CUDA-Aware adaptation of Microsoft CNTK DL framework. We report up to 47 improvement in training time for CNTK using the proposed hierarchical MPI_Bcast design. Availability of large data sets like ImageNet and massively parallel computation support in modern HPC devices like NVIDIA GPUs have fueled a renewed interest in Deep Learning (DL) algorithms. This has triggered the development of DL frameworks like Caffe, Torch, TensorFlow, and CNTK. However, most DL frameworks have been limited to a single node. In order to scale out DL frameworks and bring HPC capabilities to the DL arena, we propose, S-Caffe; a scalable and distributed Caffe adaptation for modern multi-GPU clusters. With an in-depth analysis of new requirements brought forward by the DL frameworks and limitations of current communication runtimes, we present a co-design of the Caffe framework and the MVAPICH2-GDR MPI runtime. Using the co-design methodology, we modify Caffe's workflow to maximize the overlap of computation and communication with multi-stage data propagation and gradient aggregation schemes. We bring DL-Awareness to the MPI runtime by proposing a hierarchical reduction design that benefits from CUDA-Aware features and provides up to a massive 133x speedup over OpenMPI and 2.6x speedup over MVAPICH2 for 160 GPUs. S-Caffe successfully scales up to 160 K-80 GPUs for GoogLeNet (ImageNet) with a speedup of 2.5x over 32 GPUs. To the best of our knowledge, this is the first framework that scales up to 160 GPUs. Furthermore, even for single node training, S-Caffe shows an improvement of 14 and 9 over Nvidia's optimized Caffe for 8 and 16 GPUs, respectively. In addition, S-Caffe achieves up to 1395 samples per second for the AlexNet model, which is comparable to the performance of Microsoft CNTK. We report results of the performance test of GPUs obtained using the conjugate gradient (CG) algorithm for staggered fermions on the MILC fine lattice ( @math ). We use GPUs of nVIDIA GTX 295 model for the test. When we turn off the MPI communication and use only a single GPU, the performance is 35 giga flops in double precision, which corresponds to 47 of the peak. When we turn on the MPI communication and use multi-GPUs, the performance is reduced down to 12.3 giga flops. The data transfer through the infiniband network and PCI-E bus I O is a main bottle neck. We suggest two potential solutions of how to optimize the data transfer. Designing high-performance and scalable applications on GPU clusters requires tackling several challenges. The key challenge is the separate host memory and device memory, which requires programmers to use multiple programming models, such as CUDA and MPI, to operate on data in different memory spaces. This challenge becomes more difficult to tackle when non-contiguous data in multidimensional structures is used by real-world applications. These challenges limit the programming productivity and the application performance. We propose the GPU-Aware MPI to support data communication from GPU to GPU using standard MPI. It unifies the separate memory spaces, and avoids explicit CPU-GPU data movement and CPU GPU buffer management. It supports all MPI datatypes on device memory with two algorithms: a GPU datatype vectorization algorithm and a vector based GPU kernel data pack and unpack algorithm. A pipeline is designed to overlap the non-contiguous data packing and unpacking on GPUs, the data movement on the PCIe, and the RDMA data transfer on the network. We incorporate our design with the open-source MPI library MVAPICH2 and optimize a production application: the multiphase 3D LBM. Besides the increase of programming productivity, we observe up to 19.9 percent improvement in application-level performance on 64 GPUs of the Oakley supercomputer. Abstract Documents in computer-readable form can be used to provide information about other documents, i.e. those they cite. To do this efficiently requires procedures for computer recognition of citing statements. This is not easy, especially for multi-sentence citing statements. Computer recognition procedures have been developed which are accurate to the following extent: 73 of the words in statements selected by computer procedures as being citing statements are words which are correctly attributable to the corresponding documents. The retrieval effectiveness of computer-recognized citing statements was tested in the following way. First, for eight retrieval requests in inorganiic chemistry, average recall by search of Chemical Abstracts Service indexing and Chemical Abstracts abstract text words was found to be 50 . Words from citing statements referring to the papers to be retrieved were then added to the index terms and abstract words as additional access points, and searching was repeated. Average recall increased to 70 . Only words from citing statements published within a year of the cited papers were used. The retrieval effect of citing statement words alone (published within a year) without index or abstract terms was the following: average recall was 40 . When just the words of the titles of the cited papers were added to those citing statement words, average recall increased to 50 . Scaling the distributed deep learning to a massive GPU cluster level is challenging due to the instability of the large mini-batch training and the overhead of the gradient synchronization. We address the instability of the large mini-batch training with batch-size control and label smoothing. We address the overhead of the gradient synchronization with 2D-Torus all-reduce. Specifically, 2D-Torus all-reduce arranges GPUs in a logical 2D grid and performs a series of collective operation in different orientations. These two techniques are implemented with Neural Network Libraries (NNL). We have successfully trained ImageNet ResNet-50 in 122 seconds without significant accuracy loss on ABCI cluster. Scholars are increasingly citing electronic “web references” which are not preserved in libraries or full text archives. WebCite is a new standard for citing web references. To “webcite” a document involves archiving the cited Web page through www.webcitation.org and citing the WebCite permalink instead of (or in addition to) the unstable live Web page. This journal has amended its “instructions for authors” accordingly, asking authors to archive cited Web pages before submitting a manuscript. Almost 200 other journals are already using the system. We discuss the rationale for WebCite, its technology, and how scholars, editors, and publishers can benefit from the service. Citing scholars initiate an archiving process of all cited Web references, ideally before they submit a manuscript. Authors of online documents and websites which are expected to be cited by others can ensure that their work is permanently available by creating an archived copy using WebCite and providing the citation information including the WebCite link on their Web document(s). Editors should ask their authors to cache all cited Web addresses (Uniform Resource Locators, or URLs) “prospectively” before submitting their manuscripts to their journal. Editors and publishers should also instruct their copyeditors to cache cited Web material if the author has not done so already. Finally, WebCite can process publisher submitted “citing articles” (submitted for example as eXtensible Markup Language [XML] documents) to automatically archive all cited Web pages shortly before or on publication. Finally, WebCite can act as a focussed crawler, caching retrospectively references of already published articles. Copyright issues are addressed by honouring respective Internet standards (robot exclusion files, no-cache and no-archive tags). Long-term preservation is ensured by agreements with libraries and digital preservation organizations. The resulting WebCite Index may also have applications for research assessment exercises, being able to measure the impact of Web services and published Web documents through access and Web citation metrics. @PARASPLIT [J Med Internet Res 2005;7(5):e60] General-Purpose Graphics Processing Units (GPGPUs) are becoming a common component of modern supercomputing systems. Many MPI applications are being modified to take advantage of the superior compute potential offered by GPUs. To facilitate this process, many MPI libraries are being extended to support MPI communication from GPU device memory. However, there is lack of a standardized benchmark suite that helps users evaluate common communication models on GPU clusters and do a fair comparison for different MPI libraries. In this paper, we extend the widely used OSU Micro-Benchmarks (OMB) suite with benchmarks that evaluate performance of point-point, multi-pair and collective MPI communication for different GPU cluster configurations. Benefits of the proposed benchmarks for MVAPICH2 and OpenMPI libraries are illustrated. A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results. Allan Bloom's cytologic epistemology invites further analysis. A scientific article is like a cell that interacts with its neighbors to form an organ-like cluster—a set of articles or a "literature" addressed to a common set of problems and topics. These articles interact by citing one another—by conversing in print. The clusters themselves can be seen as interacting, to varying degrees, with other clusters. This essay will focus on certain failures of intercluster communication. Synchronized stochastic gradient descent (SGD) optimizers with data parallelism are widely used in training large-scale deep neural networks. Although using larger mini-batch sizes can improve the system scalability by reducing the communication-to-computation ratio, it may hurt the generalization ability of the models. To this end, we build a highly scalable deep learning training system for dense GPU clusters with three main contributions: (1) We propose a mixed-precision training method that significantly improves the training throughput of a single GPU without losing accuracy. (2) We propose an optimization approach for extremely large mini-batch size (up to 64k) that can train CNN models on the ImageNet dataset without losing accuracy. (3) We propose highly optimized all-reduce algorithms that achieve up to 3x and 11x speedup on AlexNet and ResNet-50 respectively than NCCL-based training on a cluster with 1024 Tesla P40 GPUs. On training ResNet-50 with 90 epochs, the state-of-the-art GPU-based system with 1024 Tesla P100 GPUs spent 15 minutes and achieved 74.9 top-1 test accuracy, and another KNL-based system with 2048 Intel KNLs spent 20 minutes and achieved 75.4 accuracy. Our training system can achieve 75.8 top-1 test accuracy in only 6.6 minutes using 2048 Tesla P40 GPUs. When training AlexNet with 95 epochs, our system can achieve 58.7 top-1 test accuracy within 4 minutes, which also outperforms all other existing systems. Prior work has shown dramatic acceleration for various database operations on GPUs, but only using primitives that are not part of conventional database languages such as SQL. This paper implements a subset of the SQLite command processor directly on the GPU. This dramatically reduces the effort required to achieve GPU acceleration by avoiding the need for database programmers to use new programming languages such as CUDA or modify their programs to use non-SQL libraries. This paper focuses on accelerating SELECT queries and describes the considerations in an efficient GPU implementation of the SQLite command processor. Results on an NVIDIA Tesla C1060 achieve speedups of 20-70X depending on the size of the result set. Todays HPC systems are increasingly utilizing accelerators to lower time to solution for their users and reduce power consumption. To utilize the higher performance and energy efficiency of these accelerators, application developers need to rewrite at least parts of their codes. Taking the C++ flow solver ZFS as an example, we show that the directive-based programming model allows one to achieve good performance with reasonable effort, even for mature codes with many lines of code. Using OpenACC directives permitted us to incrementally accelerate ZFS, focusing on the parts of the program that are relevant for the problem at hand. The two new OpenACC 2.0 features, unstructured data regions and atomics, are required for this. OpenACC's interoperability with existing GPU libraries via the host_data use_device construct allowed to use CUDA-aware MPI to achieve multi-GPU scalability comparable to the CPU version of ZFS. Like many other codes, the data structures of ZFS have been designed with traditional CPUs and their relatively large private caches in mind. This leads to suboptimal memory access patterns on accelerators, such as GPUs. We show how the texture cache on NVIDIA GPUs can be used to minimize the performance impact of these suboptimal patterns without writing platform specific code. For the kernel most affected by the memory access pattern, we compare the initial array of structures memory layout with a structure of arrays layout. MSRLM is the release of our internal language modeling tool chain used in Microsoft Research. It was used in our submission for NIST MT 2006. The main difference with other freely available tools is that it was designed to scale to large amounts of data. We successfully built a language model on high end hardware on 40 billion words of web data within 8 hours. It only supports a minimal set of features. Large gigaword language models may be consumed in a first pass machine translation decoding without further processing. This document describes the implementation and usage of the tools summarily. It is our stated goal and hope that this release will be useful to the scientific community. The tool may not be used in a commercial product, or to build models used in a commercial product, or in for any commercial purpose. In addition, we require that you kindly cite this technical report when publishing results derived with this language model tool chain. This describes the LM tool which is available as: http: research.microsoft.com research downloads details 78e26f9c-fc9a-44bb-80a769324c62df8c details.aspx A new form of document coupling called co-citation is defined as the frequency with which two documents are cited together. The co-citation frequency of two scientific papers can be determined by comparing lists of citing documents in the Science Citation Index and counting identical entries. Networks of co-cited papers can be generated for specific scientific specialties, and an example is drawn from the literature of particle physics. Co-citation patterns are found to differ significantly from bibliographic coupling patterns, but to agree generally with patterns of direct citation. Clusters of co-cited papers provide a new way to study the specialty structure of science. They may provide a new approach to indexing and to the creation of SDI profiles. Modern graphics processing units (GPUs) with many-core architectures have emerged as general-purpose parallel computing platforms that can accelerate simulation science applications tremendously. While multiGPU workstations with several TeraFLOPS of peak computing power are available to accelerate computational problems, larger problems require even more resources. Conventional clusters of central processing units (CPU) are now being augmented with multiple GPUs in each compute-node to tackle large problems. The heterogeneous architecture of a multi-GPU cluster with a deep memory hierarchy creates unique challenges in developing scalable and efficient simulation codes. In this study, we pursue mixed MPI-CUDA implementations and investigate three strategies to probe the efficiency and scalability of incompressible flow computations on the Lincoln Tesla cluster at the National Center for Supercomputing Applications (NCSA). We exploit some of the advanced features of MPI and CUDA programming to overlap both GPU data transfer and MPI communications with computations on the GPU. We sustain approximately 2.4 TeraFLOPS on the 64 nodes of the NCSA Lincoln Tesla cluster using 128 GPUs with a total of 30,720 processing elements. Our results demonstrate that multi-GPU clusters can substantially accelerate computational fluid dynamics (CFD) simulations. In a document network such as a citation network of scientific documents, web-logs, etc., the content produced by authors exhibits their interest in certain topics. In addition some authors influence other authors' interests. In this work, we propose to model the influence of cited authors along with the interests of citing authors. Moreover, we hypothesize that apart from the citations present in documents, the context surrounding the citation mention provides extra topical information about the cited authors. However, associating terms in the context to the cited authors remains an open problem. We propose novel document generation schemes that incorporate the context while simultaneously modeling the interests of citing authors and influence of the cited authors. Our experiments show significant improvements over baseline models for various evaluation criteria such as link prediction between document and cited author, and quantitatively explaining unseen text. In recent years scholars have built maps of science by connecting the academic fields that cite each other, are cited together, or that cite a similar literature. But since scholars cannot always publish in the fields they cite, or that cite them, these science maps are only rough proxies for the potential of a scholar, organization, or country, to enter a new academic field. Here we use a large dataset of scholarly publications disambiguated at the individual level to create a map of science-or research space-where links connect pairs of fields based on the probability that an individual has published in both of them. We find that the research space is a significantly more accurate predictor of the fields that individuals and organizations will enter in the future than citation based science maps. At the country level, however, the research space and citations based science maps are equally accurate. These findings show that data on career trajectories-the set of fields that individuals have previously published in-provide more accurate predictors of future research output for more focalized units-such as individuals or organizations-than citation based science maps.