Prometheus:在多核系统上对基于任务的应用程序进行可伸缩和精确的仿真

Gokcen Kestor, R. Gioiosa, D. Chavarría-Miranda
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引用次数: 9

摘要

在未来多核系统上对不确定性并行应用程序的性能进行建模需要开发新的仿真和仿真技术和工具。我们提出了“Prometheus”,一个快速,准确和模块化的仿真框架,用于基于任务的应用程序。通过提高抽象级别和关注运行时同步,Prometheus可以准确地预测应用程序在非常大的多核系统上的性能。我们在两个真实平台(AMD Interlagos和Intel MIC)上验证了我们的仿真框架,报告的错误率通常低于4%。然后,我们评估了Prometheus的性能和可伸缩性:我们的结果表明,Prometheus可以在11.5小时内在512K内核的系统上模拟基于任务的应用程序。我们提供了两个测试用例,它们展示了如何使用Prometheus来研究系统的性能和行为,这些系统具有百亿级超级计算机节点所期望的一些特征,例如有源电源管理和具有大量核心但每个核心缓存减少的处理器。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prometheus: scalable and accurate emulation of task-based applications on many-core systems
Modeling the performance of non-deterministic parallel applications on future many-core systems requires the development of novel simulation and emulation techniques and tools. We present "Prometheus", a fast, accurate and modular emulation framework for task-based applications. By raising the level of abstraction and focusing on runtime synchronization, Prometheus can accurately predict applications' performance on very large many-core systems. We validate our emulation framework against two real platforms (AMD Interlagos and Intel MIC) and report error rates generally below 4%.We, then, evaluate Prometheus' performance and scalability: our results show that Prometheus can emulate a task-based application on a system with 512K cores in 11.5 hours. We present two test cases that show how Prometheus can be used to study the performance and behavior of systems that present some of the characteristics expected from exascale supercomputer nodes, such as active power management and processors with a high number of cores but reduced cache per core.
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