有效的检查点/重新启动CUDA应用程序

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

Parallel Computing Pub Date : 2023-07-01 DOI:10.1016/j.parco.2023.103018

Akira Nukada , Taichiro Suzuki , Satoshi Matsuoka

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引用次数: 0

摘要

我们介绍了NVCR，它可以实现CUDA应用程序的透明检查点和重启。NVCR是BLCR和DMTCP等主要系统级检查点软件的扩展，采用代理进程，应用程序通过代理进程访问GPU设备，以提高与最新CUDA运行时软件的兼容性。为了减少进程间通信的开销，NVCR有效地使用SYSV IPC共享内存作为CUDA固定内存。使用微基准测试和Amber作为实际应用程序进行的性能评估表明，NVCR的开销低得可以接受。

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Efficient checkpoint/Restart of CUDA applications

We present NVCR which enables transparent checkpoint and restart of CUDA applications. NVCR, works as an extension of major system-level checkpoint software such as BLCR and DMTCP, employs proxy-process and application accesses GPU devices via the proxy-process to improve the compatibility with latest CUDA runtime software. To reduce the overhead of inter-process communications, NVCR efficiently uses SYSV IPC shared memory as CUDA pinned memory. Performance evaluations using micro benchmarks and Amber as a real application show that NVCR’ overhead is acceptably low.

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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