Diagnosis, Tuning, and Redesign for Multicore Performance: A Case Study of the Fast Multipole Method
Diagnosis, Tuning, and Redesign for Multicore Performance: A Case Study of the Fast Multipole Method
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多核性能的诊断、调整和重新设计：快速多极方法的案例研究
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发表时间：
2010
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通讯作者：
R. Vuduc
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作者：
Aparna Chandramowlishwaran;Kamesh Madduri;R. Vuduc
给定一个程序和多功能系统，我们在改善快速多物方法内节点可扩展性（FMM）的背景下描述了一种方法系统的建模，分析和调整步骤的顺序，从简单的模型开始，并逐渐增加了对FMM的更深入的性能理解和更好的可扩展性的复杂性。我们可以显着提高节点内的可伸缩性；代码，匹配或超越最先进的GPGPU实现。 （最终用户程序员）以及自动化性能分析和调整工具本身可以使用。
Given a program and a multisocket, multicore system, what is the process by which one understands and improves its performance and scalability? We describe an approach in the context of improving within-node scalability of the fast multipole method (FMM). Our process consists of a systematic sequence of modeling, analysis, and tuning steps, beginning with simple models, and gradually increasing their complexity in the quest for deeper performance understanding and better scalability. For the FMM, we significantly improve within-node scalability; for example, on a quad-socket Intel Nehalem-EX system, we show speedups of 1.7× over the previous best multithreaded implementation, 19.3× over a sequential but highly tuned (e.g., SIMD-vectorized) code, and match or outperform a state-of- the-art GPGPU implementation. Our study sheds new light on the form of a more general performance analysis and tuning process that other multicore/manycore tuning practitioners (end- user programmers) and automated performance analysis and tuning tools could themselves apply.