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Managing Energy-Performance Tradeoffs for Multithreaded Applications on Multiprocessor Architectures

机译:在多处理器体系结构上管理多线程应用程序的能源性能折衷

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In modern computers, non-performance metrics such as energy consumption have become increasingly important, requiring tradeoff with performance. A recent work has proposed performance-guaranteed energy management, but it is designed specifically for sequential applications and cannot be used to a large class of multithreaded applications running on high end computers and data servers.rnTo address the above problem, this paper makes the first attempt to provide performance-guaranteed energy management for multithreaded applications on multiprocessor architectures. We first conduct a comprehensive study on the effects of energy adaptation on thread synchronizations and show that a multithreaded application suffers from not only local slowdowns due to energy adaptation, but also significant slowdowns propagated from other threads because of synchronization. Based on these findings, we design three Synchronization-Aware (SA) algorithms, LWT (Lock Waiting Time-based), CSL (Critical Section Length-based) and ODP (Operation Delay Propagation-based) algorithms, to estimate the energy adaptation-induced slowdowns on each thread. The local slowdowns are then combined across multiple threads via three aggregation methods (MAX, AVG and SUM) to estimate the overall application slowdown.rnWe evaluate our methods using a large multithreaded commercial application, IBM DB2 with industrial-strength online transaction processing (OLTP) workloads, and six SPLASH parallel scientific applications. Our experimental results show that LWT combined with the MAX aggregation method not only controls the performance slowdown within the specified limits but also conserves the most energy.
机译:在现代计算机中,诸如能耗之类的非性能指标变得越来越重要,需要权衡性能。最近的一项工作提出了可保证性能的能源管理,但是它是专为顺序应用程序设计的,不能用于在高端计算机和数据服务器上运行的大型多线程应用程序。为解决上述问题,本文首先提出了这一点。尝试为多处理器体系结构上的多线程应用程序提供性能保证的能源管理。我们首先对能量适应对线程同步的影响进行了全面的研究,结果表明,多线程应用程序不仅会因能量适应而遭受局部速度下降,而且还会因同步而遭受其他线程传播的明显速度下降。基于这些发现,我们设计了三种同步感知(SA)算法,即LWT(基于锁定等待时间),CSL(基于临界截面长度)和ODP(基于操作延迟传播)算法,以估算能量适应性,导致每个线程变慢。然后,通过三种聚合方法(MAX,AVG和SUM)在多个线程之间组合本地速度下降,以估计整个应用程序速度下降。rn我们使用大型多线程商业应用程序IBM DB2和具有工业强度在线事务处理(OLTP)的方法来评估我们的方法工作量和六个SPLASH并行科学应用程序。我们的实验结果表明,LWT与MAX聚合方法的结合不仅可以将性能下降控制在指定的范围内,而且可以节省最多的能量。

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