An exploration of heterogeneous systems

Jesús Carabaño, Francisco Dios, M. Daneshtalab, M. Ebrahimi
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引用次数: 10

Abstract

Heterogeneous computing represents a trendy way to achieve further scalability in the high-performance computing area. It aims to join different processing units in a networked-based system such that each task is preferably executed by the unit which is able to efficiently perform that task. Memory hierarchy, instruction set, control logic, and other properties may differ in processing units so as to be specialized for different variety of problems. However, it will be more time-consuming for computer engineers to understand, design, and program on these systems. On the other hand, proper problems running on well-chosen heterogeneous systems present higher performance and superior energy efficiency. Such balance of attributes seldom makes a heterogeneous system useful for other fields than embedded computing or high-performance computing. Among them, embedded computing is more area and energy efficient while high-performance computing obtains more performance. GPUs, FPGAs or the new Xeon Phi are example of common computational units that, along with CPUs, can compose heterogeneous systems aiming to accelerate the execution of programs. In this paper, we have explored these architectures in terms of energy efficiency, performance, and productivity.
异质系统的探索
异构计算代表了在高性能计算领域实现进一步可伸缩性的一种流行方式。它旨在将基于网络的系统中的不同处理单元联接起来,使得每个任务优选地由能够有效地执行该任务的单元执行。存储器层次结构、指令集、控制逻辑和其他属性在处理单元中可能不同,以便专门用于不同种类的问题。然而,对于计算机工程师来说,在这些系统上理解、设计和编程将更加耗时。另一方面,在选择良好的异构系统上运行适当的问题可以获得更高的性能和更高的能源效率。这种属性的平衡很少使异构系统在嵌入式计算或高性能计算之外的其他领域有用。其中,嵌入式计算具有更高的面积和能效,而高性能计算具有更高的性能。gpu、fpga或新的Xeon Phi是常见计算单元的例子,它们与cpu一起可以组成旨在加速程序执行的异构系统。在本文中,我们从能源效率、性能和生产力的角度探讨了这些体系结构。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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