首页> 外文会议>International Conference on Scalable Computing and Communications >CMP Thread Assignment Based on Group sharing L2 Cache
【24h】

CMP Thread Assignment Based on Group sharing L2 Cache

机译:基于组共享L2缓存的CMP线程分配

获取原文

摘要

With the development of electric technology, uniprocessor is being substituted by CMP (chip multi processors). CMP can run multi-thread program efficiently, many researchers engage in the study of multi-thread, including extracting multi-thread from legacy single thread program and making standardization for future multi-thread program. Data communication among threads is an inevitable problem in multi-thread program, and efficient data sharing is an important aspect for program performance. But researchers focus data sharing on memory organization and relationship among threads, there is little attention for intra-processor. In this paper, we develop a thread assignment method for group sharing L2 cache architecture according to the data relationship among threads. We allocate some threads to some cores and some threads others. In our experiment, we simulates four threads with different degree data sharing and running in four cores CMP, whose cores is divided into two groups. Comparing some program execution tracks, we find that the main difference between two simulations is the hit rate of L2 cache and thread assignment brings 6.25% running time improvement. The L2 cache hit rate is 91.0% and 87.1% with thread assignment our proposed, but the L2 cache hit rate is 77.0% and 75.4% with random thread assignment. It descends 14.0% and 11.7% for each group.
机译:随着电气技术的发展,单处理器正在被CMP(芯片多处理器)代替。 CMP可以有效地运行多线程程序,许多研究人员从事多线程的研究,包括从遗留单线程中提取多线程,并为未来的多线程程序进行标准化。线程之间的数据通信是多线程程序中的不可避免的问题,有效的数据共享是程序性能的重要方面。但研究人员焦点数据共享对内存组织和线程之间的关系,内部处理器几乎没有关注。在本文中,我们根据线程之间的数据关系开发了用于组共享L2缓存架构的线程分配方法。我们将一些线程分配给一些核心和一些线程。在我们的实验中,我们模拟了四个带有不同程度的数据共享的线程,并以四个核心CMP运行,其核心分为两组。比较一些程序执行轨道,我们发现两种模拟之间的主要区别是L2缓存和线程分配的命中率,带来了6.25%运行时间改进。 L2缓存命中率为91.0%和87.1%,我们提出了线程分配,但L2缓存命中率为77.0%和75.4%,随机线程分配为77.0%和75.4%。每组下降14.0%和11.7%。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号