您当前所在位置: 首页 > 学者

陈云霁

  • 10浏览

  • 0点赞

  • 0收藏

  • 0分享

  • 0下载

  • 0评论

  • 引用

期刊论文

Performance Portability Across Heterogeneous SoCs Using a Generalized Library-Based Approach

暂无

ACM Transactions on Architecture and Code Optimization,2014,11(2):21 | 2014年06月01日 | doi.org/10.1145/2608253

URL:https://dl.acm.org/doi/10.1145/2608253

摘要/描述

Because of tight power and energy constraints, industry is progressively shifting toward heterogeneous system-on-chip (SoC) architectures composed of a mix of general-purpose cores along with a number of accelerators. However, such SoC architectures can be very challenging to efficiently program for the vast majority of programmers, due to numerous programming approaches and languages. Libraries, on the other hand, provide a simple way to let programmers take advantage of complex architectures, which does not require programmers to acquire new accelerator-specific or domain-specific languages. Increasingly, library-based, also called algorithm-centric, programming approaches propose to generalize the usage of libraries and to compose programs around these libraries, instead of using libraries as mere complements. In this article, we present a software framework for achieving performance portability by leveraging a generalized library-based approach. Inspired by the notion of a component, as employed in software engineering and HW/SW codesign, we advocate nonexpert programmers to write simple wrapper code around existing libraries to provide simple but necessary semantic information to the runtime. To achieve performance portability, the runtime employs machine learning (simulated annealing) to select the most appropriate accelerator and its parameters for a given algorithm. This selection factors in the possibly complex composition of algorithms used in the application, the communication among the various accelerators, and the tradeoff between different objectives (i.e., accuracy, performance, and energy). Using a set of benchmarks run on a real heterogeneous SoC composed of a multicore processor and a GPU, we show that the runtime overhead is fairly small at 5.1% for the GPU and 6.4% for the multi-core. We then apply our accelerator selection approach to a simulated SoC platform containing multiple inexact accelerators. We show that accelerator selection together with hardware parameter tuning achieves an average 46.2% energy reduction and a speedup of 2.1× while meeting the desired application error target.

关键词:

【免责声明】以下全部内容由[陈云霁]上传于[2020年11月04日 14时14分26秒],版权归原创者所有。本文仅代表作者本人观点,与本网站无关。本网站对文中陈述、观点判断保持中立,不对所包含内容的准确性、可靠性或完整性提供任何明示或暗示的保证。请读者仅作参考,并请自行承担全部责任。

我要评论

全部评论 0

本学者其他成果

    同领域成果