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首页> 外文期刊>IEEE Transactions on Computers >PARMA: Parallelization-Aware Run-Time Management for Energy-Efficient Many-Core Systems
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PARMA: Parallelization-Aware Run-Time Management for Energy-Efficient Many-Core Systems

机译:帕尔马:节能的许多核心系统的并行化感知运行时管理

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摘要

Performance and energy efficiency considerations have shifted computing paradigms from single-core to many-core architectures. At the same time, traditional speedup models such as Amdahl's Law face challenges in the run-time reasoning for system performance and energy efficiency, because these models typically assume limited variations of the parallel fraction. Moreover, the parallel fraction, which varies dynamically in workloads, is generally unknown at run-time without application-level instrumentation. This article describes novel performance/energy trade-off models based on realistic architectural considerations, which describe the parallel fraction and speedup as functions of performance counter values available in modern processors, removing the need for application-level instrumentation. These are then used to develop a Parallelization-Aware Run-time Management (PARMA) approach. PARMA aims at controlling core allocations and operating voltage/frequency points for energy efficiency, according to the varying workload parallel fractions. The efficacy of our models and the PARMA approach is extensively validated using a number of PARSEC benchmark applications, involving two performance/energy trade-off metrics: energy-delay-product (EDP), typically used in high-performance applications and energy per instruction (EPI), suitable for energy-aware applications. Up to 48 and 68 percent improvements in EDP and EPI have been observed using the PARMA approach compared with parallelization-agnostic methods.
机译:性能和能源效率考虑已将计算范例从单核转移到许多核心架构。与此同时,传统的加速模型如Amdahl的法律面临挑战,在运行时间推理中进行系统性能和能效,因为这些模型通常承担有限的平行分数的变化。此外,在工作负载中动态变化的平行分数通常在没有应用级别仪器的运行时间内未知。本文介绍了基于现实架构考虑的新颖性能/能源折衷模型,它将并行分数和加速作为现代处理器中可用的性能计数器值的功能,取消了应用程序级仪器的需求。然后使用这些来开发并行化感知运行时管理(帕尔马)方法。帕尔马旨在根据变化的工作负载平行分数控制核心分配和操作电压/频率点,以实现能效。我们的模型和帕尔马方法的功效广泛验证了许多PARSEC基准应用,涉及两个性能/能源折衷度量:能源延迟产品(EDP),通常用于每个指令的高性能应用和能量(EPI),适用于能量感知应用。使用Parma方法与平行化 - 不可知方法相比,使用帕尔马方法观察到高达48和68%的改善。

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