改进在gpu和多核上随机运行的密码分析应用程序

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

Parallel Computing Pub Date : 2022-09-01 DOI:10.1016/j.parco.2022.102944

Lena Oden, Jörg Keller

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引用次数: 0

摘要

我们研究了由许多独立任务组成的密码分析应用程序，这些任务表现出随机的运行时分布。我们比较了在gpu和带有SIMD单元的多核cpu上执行此类应用程序的四种算法。我们证明，对于四种不同的发行版、多种问题大小和三种平台，最佳策略各不相同。我们通过在基于英特尔skylake的多核CPU和高性能GPU (Nvidia Volta)上进行大量实验来支持我们的分析结果。

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Improving cryptanalytic applications with stochastic runtimes on GPUs and multicores

We investigate cryptanalytic applications comprised of many independent tasks that exhibit a stochastic runtime distribution. We compare four algorithms for executing such applications on GPUs and on multicore CPUs with SIMD units. We demonstrate that for four different distributions, multiple problem sizes, and three platforms the best strategy varies. We support our analytic results by extensive experiments on an Intel Skylake-based multicore CPU and a high performance GPU (Nvidia Volta).

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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