A Template-based Framework for Exploring Coarse-Grained Reconfigurable Architectures

Artur Podobas, K. Sano, S. Matsuoka
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引用次数: 19

Abstract

Coarse-Grained Reconfigurable Architectures (CGRAs) are being considered as a complementary addition to modern High-Performance Computing (HPC) systems. These reconfigurable devices overcome many of the limitations of the (more popular) FPGA, by providing higher operating frequency, denser compute capacity, and lower power consumption. Today, CGRAs have been used in several embedded applications, including automobile, telecommunication, and mobile systems, but the literature on CGRAs in HPC is sparse and the field full of research opportunities. In this work, we introduce our CGRA simulator infrastructure for use in evaluating future HPC CGRA systems. Our CGRA simulator is built on synthesizable VHDL and is highly parametrizable, including support for connectivity, SIMD, data-type width, and heterogeneity. Unlike other related work, our framework supports co-integration with third-party memory simulators or evaluation of future memory architecture, which is crucial to reason around memory-bound applications. We demonstrate how our framework can be used to explore the performance of multiple different kernels, showing the impact of different configuration and design-space options.
用于探索粗粒度可重构架构的基于模板的框架
粗粒度可重构体系结构(CGRAs)被认为是现代高性能计算(HPC)系统的补充。这些可重构器件通过提供更高的工作频率、更密集的计算能力和更低的功耗,克服了(更流行的)FPGA的许多限制。目前,CGRAs已应用于多个嵌入式应用,包括汽车、电信和移动系统,但关于CGRAs在高性能计算中的研究文献很少,该领域充满了研究机会。在这项工作中,我们介绍了我们的CGRA模拟器基础设施,用于评估未来的HPC CGRA系统。我们的CGRA模拟器建立在可合成的VHDL上,具有高度的参数化,包括对连接性、SIMD、数据类型宽度和异构性的支持。与其他相关工作不同,我们的框架支持与第三方内存模拟器的协集成或对未来内存体系结构的评估,这对于推断内存绑定应用程序至关重要。我们将演示如何使用我们的框架来探索多个不同内核的性能,展示不同配置和设计空间选项的影响。
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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