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检索条件"任意字段=International Parallel and Distributed Processing Symposium"

共 11367 条记录，以下是61-70 订阅

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Benchmarking and Dissecting the Nvidia Hopper GPU Architecture 38

Benchmarking and Dissecting the Nvidia Hopper GPU Architectu...

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international parallel and distributed processing symposium (IPDPS)

作者： Luo, Weile Fan, Ruibo Li, Zeyu Du, Dayou Wang, Qiang Chu, Xiaowen Hong Kong Univ Sci & Technol Guangzhou Hong Kong Peoples R China Harbin Inst Technol Shenzhen Peoples R China

ISBN: (纸本)9798350387117;9798350387124

Graphics processing units (GPUs) are continually evolving to cater to the computational demands of contemporary general-purpose workloads, particularly those driven by artificial intelligence (AI) utilizing deep learning techniques. A substantial body of studies have been dedicated to dissecting the micro-architectural metrics characterizing diverse GPU generations, which helps researchers understand the hardware details and leverage them to optimize the GPU programs. However, the latest Hopper GPUs present a set of novel attributes, including new tensor cores supporting FP8, DPX, and distributed shared memory. Their details still remain mysterious in terms of performance and operational characteristics. In this research, we propose an extensive benchmarking study focused on the Hopper GPU. The objective is to unveil its microarchitectural intricacies through an examination of the new instruction-set architecture (ISA) of Nvidia GPUs and the utilization of new CUDA APIs. Our approach involves two main aspects. Firstly, we conduct conventional latency and throughput comparison benchmarks across the three most recent GPU architectures, namely Hopper, Ada, and Ampere. Secondly, we delve into a comprehensive discussion and benchmarking of the latest Hopper features, encompassing the Hopper DPX dynamic programming (DP) instruction set, distributed shared memory, and the availability of FP8 tensor cores. The microbenchmarking results we present offer a deeper understanding of the novel GPU AI function units and programming features introduced by the Hopper architecture. This newfound understanding is expected to greatly facilitate software optimization and modeling efforts for GPU architectures. To the best of our knowledge, this study makes the first attempt to demystify the tensor core performance and programming instruction sets unique to Hopper GPUs.

关键词： Instruction Latency Tensor Core PTX Hopper DPX Asynchronous Execution distributed Shared Memory

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Ray-based Elastic distributed Data parallel Framework with distributed Data Cache

Ray-based Elastic Distributed Data Parallel Framework with D...

引用

37th IEEE international parallel and distributed processing symposium (IPDPS)

作者： Lin, Haoran Qin, Xinwei Qiu, Shuang Sun, Yi Yin, Zekun Liu, Weiguo Shandong Univ Sch Software Jinan Peoples R China SZ DJI Technol Co Ltd Shenzhen Peoples R China

ISBN: (纸本)9798350311990

With the development of large-scale machine learning, distributed data parallel has become the de facto standard strategy for model training. However, when training model using distributed data parallel on large-scale clusters, some unexpected factors may lead to the failure of the training tasks. Thus, a high-performance, scalabe, yet fault-tolerant distributed training framework is urgently needed. Most commonly used opensourced distributed training frameworks (e.g., PyTorch) do not fully meet this need. In this paper, we have designed an elastic distributed training framework based on Ray (a high-performance distributed framework). Our framework takes advantage of Ray's fault-tolerant store, scalability, and the stateful actor. In our framework, training tasks will not be terminated when the number of training processes changes. Moreover, we have designed an elastic distributed data cache using Ray's object store and provided an efficient dataloader (called elastic dataloader). Performance evaluation shows that elastic dataloader is more than 2 times faster than PyTorch's Dataloader on a cluster equipped with 10 Gigabit Ethernet.

关键词： distributed data parallel elastic distributed data cache Ray

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parallel Programming with Pictures: Choosing Your Own Adventure

Parallel Programming with Pictures: Choosing Your Own Advent...

引用

37th IEEE international parallel and distributed processing symposium (IPDPS)

作者： Feng, W. Davis-Wallace, L. Virginia Tech Dept Comp Sci Blacksburg VA 24061 USA

ISBN: (纸本)9798350311990

Given the ubiquity of parallel computing hardware, we introduced parallelprogramming with pictures to the block-based Snap! environment and called it pSnap!, short for parallel Snap! We then created an accessible curriculum for students of all ages to learn how to program serially and then how to program with explicit parallelism. This paper presents a new and innovative extension to our curriculum on parallel programming with pSnap!, one that broadens its appeal to the masses by teaching the application of parallel programming as a "choose your own learning adventure" activity, inspired by the Choose Your Own Adventure book series of the 1980s and 1990s. Specifically, after students learn the basics of parallel programming with pictures, they are ready to choose their next learning adventure, which applies their newfound parallel programming skills to create a video game of their choice, i.e., Missile Command or Do You Want to Build a Snowman?

关键词： block-based programming curriculum computer science education parallel programming pictures Scratch Snap pSnap

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Hadar: Heterogeneity-Aware Optimization-Based Online Scheduling for Deep Learning Cluster 38

Hadar: Heterogeneity-Aware Optimization-Based Online Schedul...

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international parallel and distributed processing symposium (IPDPS)

作者： Sultana, Abeda Xu, Fei Yuan, Xu Chen, Li Tzeng, Nian-Feng Univ Louisiana Lafayette Sch Comp & Informat Lafayette LA USA East China Normal Univ Sch Comp Sci & Technol Shanghai Peoples R China Univ Delaware Dept Comp & Informat Sci Newark DE USA

ISBN: (纸本)9798350387117;9798350387124

With the wide adoption of deep neural network (DNN) models for various applications, enterprises, and cloud providers have built deep learning clusters and increasingly deployed specialized accelerators, such as GPUs and TPUs, for DNN training jobs. To arbitrate cluster resources among multi-user jobs, existing schedulers fall short, either lacking fine-grained heterogeneity awareness or hardly generalizable to various scheduling policies. To fill this gap, we propose a novel design of a task-level heterogeneity-aware scheduler, Hadar, based on an online optimization framework that can express other scheduling algorithms. Hadar leverages the performance traits of DNN jobs on a heterogeneous cluster, characterizes the task-level performance heterogeneity in the optimization problem, and makes scheduling decisions across both spatial and temporal dimensions. The primal-dual framework is employed, with our design of a dual subroutine, to solve the optimization problem and guide the scheduling design. Extensive trace-driven simulations with representative DNN models have been conducted to demonstrate that Hadar improves the average job completion time (JCT) by 3x over an Apache YARN-based resource manager used in production. Moreover, Hadar outperforms Gavel[1], the state-of-the-art heterogeneity-aware scheduler, by 2.5x for the average JCT, shortens the queuing delay by 13%, and improves FTF (Finish-Time-Fairness) by 1.5%.

关键词： distributed deep learning scheduling optimization

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A2FL: Autonomous and Adaptive File Layout in HPC through Real-time Access Pattern Analysis 38

A2FL: Autonomous and Adaptive File Layout in HPC through Rea...

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international parallel and distributed processing symposium (IPDPS)

作者： Sung, Dong Kyu Son, Yongseok Sim, Alex Wu, Kesheng Byna, Suren Tang, Houjun Eom, Hyeonsang Kim, Changjong Kim, Sunggon Seoul Natl Univ Seoul South Korea Chung Ang Univ Seoul South Korea Lawrence Berkeley Natl Lab Berkeley CA USA Ohio State Univ Columbus OH USA Seoul Natl Univ Sci & Technol Seoul South Korea

ISBN: (纸本)9798350387117;9798350387124

Various scientific applications with different I/O characteristics are executed in HPC systems. However, underlying parallel file systems are unaware of these characteristics of applications, and using a single fixed file layout for all applications can degrade the performance of HPC systems. In this paper, we propose A2FL, an autonomous and adaptive file layout adjustment scheme that optimizes parallel file system configurations by analyzing the access pattern of the applications. The key steps of A2FL are as follows: (1) A2FL initially intercepts the I/O operations of the application, recording their access patterns in real-time. (2) The access patterns are then transformed into a graphical representation used for predicting I/O performance and providing adjustment recommendations. (3) A2FL autonomously adjusts the file layout based on the prediction results, delivering an optimal file layout within the parallel file system. Moreover, we propose A2FL-Compound which analyzes an access pattern by dividing it into smaller components to optimize the file layout in a fine-grained manner. Our evaluations demonstrate that A2FL significantly enhances I/O performance, with improvements of up to 65.9x compared to the default file layout.

关键词： HPC I/O Access Pattern Machine Learning Storage parallel File System

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Billion-scale Detection of Isomorphic Nodes

Billion-scale Detection of Isomorphic Nodes

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37th IEEE international parallel and distributed processing symposium (IPDPS)

作者： Cappelletti, Luca Fontana, Tommaso Reese, Justin Bader, David A. Univ Milan Dept Comp Sci Milan Italy Berkeley Lab EGSB Berkeley CA USA New Jersey Inst Technol Dept Data Sci Newark NJ USA

ISBN: (纸本)9798350311990

This paper presents an algorithm for detecting attributed high-degree node isomorphism. High-degree isomorphic nodes seldom happen by chance and often represent duplicated entities or data processing errors. By definition, isomorphic nodes are topologically indistinguishable and can be problematic in graph ML tasks. The algorithm employs a parallel, "degree-bounded" approach that fingerprints each node's local properties through a hash, which constrains the search to nodes within hash-defined buckets, thus minimising the number of comparisons. This method scales on graphs with billions of nodes and edges. Finally, we provide isomorphic node oddities identified in real-world data.

关键词： graph node isomorphism parallel algorithm

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Evaluating Functional Memory-Managed parallel Languages for HPC using the NAS parallel Benchmarks

Evaluating Functional Memory-Managed Parallel Languages for ...

引用

37th IEEE international parallel and distributed processing symposium (IPDPS)

作者： Wilkins, Michael Weil, Garrett Arnold, Luke Ilardavellast, Nikos Dindat, Peter Northwestern Univ Evanston IL 60208 USA

ISBN: (纸本)9798350311990

Functional, memory-managed parallel languages (FMPLs) are a recent innovative approach to shared-memory parallel programming. Despite their rising prevalence in other areas, FMPLs have yet to gain traction in HPC. In this work, we explore the utility of FMPLs for HPC by re-implementing the NAS parallel Benchmarks in an FMPL. For this study, we ported the benchmarks into the parallel ML language. We discuss the advantages and disadvantages of using parallel ML for HPC applications based on our development experience. We compare the performance of our parallel ML implementation to the existing C/OpenMP version. The FMPL implementations are 1.02x-5.76x slower compared to OpenMP. Our positive development experience combined with some competitive performance results suggest that FMPLs have the potential to become a viable choice for HPC applications. We conclude by describing our future work to automatically manage distributed memory within an FMPL, creating a compelling new programming model for HPC.

关键词： Application programming interfaces (API)

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Error-bounded Scalable parallel Tensor Train Decomposition

Error-bounded Scalable Parallel Tensor Train Decomposition

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37th IEEE international parallel and distributed processing symposium (IPDPS)

作者： Xie, Shiyao Miura, Akinori Ono, Kenji Kyushu Univ Informat Sci & Technol Fukuoka Japan NS Solut Corp Tokyo Japan Kyushu Univ Res Inst Informat Technol Fukuoka Japan

ISBN: (纸本)9798350311990

Tensor train (TT) decomposition is a method for approximating and analysing tensors. TT-SVD, the most commonly used TT decomposition algorithm, computes the TT-format of a tensor in a sequential manner by alternately reshaping and compressing the tensor. For large tensors, this requires a large amount of computation time and memory. In this paper, we propose a distributed parallel algorithm, PTTD, to perform TT decomposition, which distributes parts of the tensor to all processes, decomposes it in parallel using TT-SVD, and merges the results to obtain the TT-format of the original tensor. Rounding is applied to reduce the size of the merged TT-formats. The algorithm is deterministic, which means that approximation error is controllable and there is no need to know the TT-ranks of the tensor in advance. Experimental results show that PTTD achieves an average speedup of 5384x using 8192 cores, and that the approximation error decreases as the number of cores increases, at the cost of slowly growing TT-ranks.

关键词： tensor train decomposition distributed tensor decomposition TT-rounding TT-format

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Massively parallel asynchronous fractal optimization

Massively parallel asynchronous fractal optimization

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37th IEEE international parallel and distributed processing symposium (IPDPS)

作者： Firmin, Thomas Talbi, El-Ghazali Univ Lille Inria Lille Nord Europe CRIStAL UMR CNRS 9189 Lille France

ISBN: (纸本)9798350311990

Fractal-based decomposition is a flexible framework representing a family of optimization algorithms based on a hierarchical decomposition of the search space. We have built a software called Zellij, in which we were able to instantiate popular decomposition-based algorithms. Our goal is to tackle optimization problems characterized by computationally expensive objective functions and high dimensional search space. In this paper, we propose a generic asynchronous parallel methodology of fractal-based optimization algorithms on multi-nodes and multi-CPUs distributed environments. Experimental results show a significantly reduced computation time between the mono-threaded version and the asynchronous one. The obtained results are also analyzed according to the various search components such as tree search, exploration, and exploitation strategies.

关键词： Asynchronous metaheuristic Continuous optimization Fractals Hierarchical decomposition

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CliZ: Optimizing Lossy Compression for Climate Datasets with Adaptive Fine-tuned Data Prediction 38

CliZ: Optimizing Lossy Compression for Climate Datasets with...

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international parallel and distributed processing symposium (IPDPS)

作者： Jian, Zizhe Di, Sheng Liu, Jinyang Zhao, Kai Liang, Xin Xu, Haiying Underwood, Robert Wu, Shixun Huang, Jiajun Chen, Zizhong Cappello, Franck Univ Calif Riverside Riverside CA USA Argonne Natl Lab Lemont IL USA Florida State Univ Tallahassee FL USA Univ Kentucky Lexington KY USA Natl Ctr Atmospher Res Boulder CO USA

ISBN: (纸本)9798350387117;9798350387124

Benefiting from the cutting-edge supercomputers that support extremely large-scale scientific simulations, climate research has advanced significantly over the past decades. However, new critical challenges have arisen regarding efficiently storing and transferring large-scale climate data among distributed repositories and databases for post hoc analysis. In this paper, we develop CliZ, an efficient online error-controlled lossy compression method with optimized data prediction and encoding methods for climate datasets across various climate models. On the one hand, we explored how to take advantage of particular properties of the climate datasets (such as mask-map information, dimension permutation/fusion, and data periodicity pattern) to improve the data prediction accuracy. On the other hand, CliZ features a novel multi-Huffman encoding method, which can significantly improve the encoding efficiency. Therefore significantly improving compression ratios. We evaluated CliZ versus many other state-of-the-art error-controlled lossy compressors (including SZ3, ZFP, SPERR, and QoZ) based on multiple real-world climate datasets with different models. Experiments show that CliZ outperforms the second-best compressor (SZ3, SPERR, or QoZ1.1) on climate datasets by 20%-200% in compression ratio. CliZ can significantly reduce the data transfer cost between the two remote Globus endpoints by 32%-38%.

关键词： error-controlled lossy compression climate datasets distributed data repository/database

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