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An Analysis of Variation Between Cores for Intel Xeon Phi Knights Corner and Xeon Phi Knights Landing.

机译:英特尔至强披披骑士角和至强披披骑士登陆的内核之间的差异分析。

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摘要

As we move towards exascale computing, the efficiency of application performance and energy utilization, must be optimized by redefining architectural features and application performance analysis. This research analyzes the performance per core of 8 applications on Intel Xeon Phi Knights Corner (KNC) and Knights Landing (KNL) to determine if performance variation within cores can lead to performance and energy improvements. Our results showed that KNC architecture's core vary in performance, leading to faster inner core performance as a result of memory characteristics and core utilization. It also shows that cores 17, 34, and 51 on the KNL architectures performs consistently slower than other cores, with core 0 performing either faster, slower or within the average performance time all the cores. A power performance study was then done utilizing different core configurations on the KNC. The results show that by targeting inner cores for applications that exhibit better inner core performance, a maximum energy reduction of 16.4% compared to a con- figuration using all cores was possible with its optimal thread configuration. Energy reduction was achieved with along with a 2% reduction in the fastest execution time of the same application. Our results also show how application characteristics lead to different core variation performances on KNC and KNL Xeon Phi architectures.
机译:随着我们向万亿级计算迈进,必须通过重新定义架构功能和应用程序性能分析来优化应用程序性能和能源利用率。这项研究分析了英特尔至强融核Knights Corner(KNC)和Knights Landing(KNL)上8个应用程序的每核性能,以确定核内的性能差异是否可以带来性能和能源的改善。我们的结果表明,KNC架构的内核性能各不相同,这是由于内存特性和内核利用率导致了更快的内部内核性能。它还显示KNL架构上的内核17、34和51始终比其他内核慢,而内核0的执行速度更快,更慢或在所有内核的平均性能时间内。然后,利用KNC上的不同内核配置进行了功率性能研究。结果表明,通过将内芯用于表现出更好内芯性能的应用,与使用所有内芯的配置相比,通过其最佳螺纹配置,最大能耗降低了16.4%。在实现相同应用程序的最快执行时间的同时,还实现了能耗降低和2%的降低。我们的结果还表明,应用特性如何导致KNC和KNL Xeon Phi架构具有不同的核心变化性能。

著录项

  • 作者

    Robinson, Jamar.;

  • 作者单位

    Clemson University.;

  • 授予单位 Clemson University.;
  • 学科 Computer engineering.
  • 学位 M.S.
  • 年度 2017
  • 页码 82 p.
  • 总页数 82
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-17 11:38:37

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