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Power monitoring with PAPI for extreme scale architectures and dataflow-based programming models

机译:使用PAPI进行电源监控以实现超大规模架构和基于数据流的编程模型

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For more than a decade, the PAPI performance-monitoring library has provided a clear, portable interface to the hardware performance counters available on all modern CPUs and other components of interest (e.g., GPUs, network, and I/O systems). Most major end-user tools that application developers use to analyze the performance of their applications rely on PAPI to gain access to these performance counters. One of the critical roadblocks on the way to larger, more complex high performance systems, has been widely identified as being the energy efficiency constraints. With modern extreme scale machines having hundreds of thousands of cores, the ability to reduce power consumption for each CPU at the software level becomes critically important, both for economic and environmental reasons. In order for PAPI to continue playing its well established role in HPC, it is pressing to provide valuable performance data that not only originates from within the processing cores but also delivers insight into the power consumption of the system as a whole. An extensive effort has been made to extend the Performance API to support power monitoring capabilities for various platforms. This paper provides detailed information about three components that allow power monitoring on the Intel Xeon Phi and Blue Gene/Q. Furthermore, we discuss the integration of PAPI in PARSEC - a task-based dataflow-driven execution engine - enabling hardware performance counter and power monitoring at true task granularity.
机译:十多年来,PAPI性能监视库为所有现代CPU和其他感兴趣的组件(例如GPU,网络和I / O系统)上可用的硬件性能计数器提供了清晰,可移植的接口。应用程序开发人员用来分析其应用程序性能的大多数主要最终用户工具都依赖于PAPI来访问这些性能计数器。通往更大,更复杂的高性能系统的关键障碍之一已被广泛认为是能源效率的制约因素。对于具有成千上万个内核的现代极限机器,从经济和环境方面考虑,在软件级别降低每个CPU功耗的能力变得至关重要。为了使PAPI在HPC中继续发挥其已确立的作用,它迫切需要提供有价值的性能数据,这些数据不仅源自处理核心,而且还可以洞悉整个系统的功耗。为了扩展Performance API以支持各种平台的电源监视功能,已经进行了广泛的努力。本文提供了有关三个组件的详细信息,这些组件允许对Intel Xeon Phi和Blue Gene / Q进行电源监视。此外,我们讨论了PAPI在基于任务的数据流驱动的执行引擎PARSEC中的集成-以真正的任务粒度启用硬件性能计数器和电源监视。

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