Tausch: A halo exchange library for large heterogeneous computing systems using MPI, OpenCL, and CUDA

IF 2 4区计算机科学 Q2 COMPUTER SCIENCE, THEORY & METHODS

Parallel Computing Pub Date : 2022-12-01 DOI:10.1016/j.parco.2022.102973

Lukas Spies , Amanda Bienz , David Moulton , Luke Olson , Andrew Reisner

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引用次数: 1

Abstract

Exchanging halo data is a common task in modern scientific computing applications and efficient handling of this operation is critical for the performance of the overall simulation. Tausch is a novel header-only library that provides a simple API for efficiently handling these types of data movements. Tausch supports both simple CPU-only systems, but also more complex heterogeneous systems with both CPUs and GPUs. It currently supports both OpenCL and CUDA for communicating with GPGPU devices, and allows for communication between GPGPUs and CPUs. The API allows for drop-in replacement in existing codes and can be used for the communication layer in new codes. This paper provides an overview of the approach taken in Tausch, and a performance analysis that demonstrates expected and achieved performance. We highlight the ease of use and performance with three applications: First Tausch is compared to the halo exchange framework from two Mantevo applications, HPCCG and miniFE, and then it is used to replace a legacy halo exchange library in the flexible multigrid solver framework Cedar.

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Tausch:一个halo交换库，用于使用MPI、OpenCL和CUDA的大型异构计算系统

在现代科学计算应用中，交换光晕数据是一项常见的任务，有效地处理这一操作对整个模拟的性能至关重要。Tausch是一个新颖的头文件库，它提供了一个简单的API来有效地处理这些类型的数据移动。Tausch既支持简单的只有cpu的系统，也支持更复杂的具有cpu和gpu的异构系统。它目前支持OpenCL和CUDA与GPGPU设备的通信，并允许GPGPU和cpu之间的通信。该API允许在现有代码中插入替换，并可用于新代码中的通信层。本文概述了Tausch采用的方法，并进行了性能分析，展示了预期的性能和已实现的性能。我们强调了三个应用程序的易用性和性能:首先将Tausch与Mantevo的两个应用程序HPCCG和miniFE的halo交换框架进行比较，然后使用Tausch取代灵活的多网格求解器框架Cedar中的遗留halo交换库。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Parallel Computing 工程技术-计算机：理论方法

CiteScore

3.50

自引率

7.10%

发文量

审稿时长

4.5 months

期刊介绍： Parallel Computing is an international journal presenting the practical use of parallel computer systems, including high performance architecture, system software, programming systems and tools, and applications. Within this context the journal covers all aspects of high-end parallel computing from single homogeneous or heterogenous computing nodes to large-scale multi-node systems. Parallel Computing features original research work and review articles as well as novel or illustrative accounts of application experience with (and techniques for) the use of parallel computers. We also welcome studies reproducing prior publications that either confirm or disprove prior published results. Particular technical areas of interest include, but are not limited to: -System software for parallel computer systems including programming languages (new languages as well as compilation techniques), operating systems (including middleware), and resource management (scheduling and load-balancing). -Enabling software including debuggers, performance tools, and system and numeric libraries. -General hardware (architecture) concepts, new technologies enabling the realization of such new concepts, and details of commercially available systems -Software engineering and productivity as it relates to parallel computing -Applications (including scientific computing, deep learning, machine learning) or tool case studies demonstrating novel ways to achieve parallelism -Performance measurement results on state-of-the-art systems -Approaches to effectively utilize large-scale parallel computing including new algorithms or algorithm analysis with demonstrated relevance to real applications using existing or next generation parallel computer architectures. -Parallel I/O systems both hardware and software -Networking technology for support of high-speed computing demonstrating the impact of high-speed computation on parallel applications