CASK:稀疏内核的开源自定义架构

Proceedings of the 2016 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays Pub Date : 2016-02-21 DOI:10.1145/2847263.2847338

Paul Grigoras, P. Burovskiy, W. Luk

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引用次数: 15

摘要

稀疏矩阵向量乘法(SpMV)是许多科学应用中的重要核心。为了提高基于FPGA的SpMV的性能和适用性，我们提出了一种利用输入矩阵特性来生成优化的自定义架构的方法。对于每个矩阵，我们的方法生成的体系结构比最坏情况下的体系结构快3.8到48倍，这显示了SpMV实例特定设计的好处。

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CASK: Open-Source Custom Architectures for Sparse Kernels

Sparse matrix vector multiplication (SpMV) is an important kernel in many scientific applications. To improve the performance and applicability of FPGA based SpMV, we propose an approach for exploiting properties of the input matrix to generate optimised custom architectures. The architectures generated by our approach are between 3.8 to 48 times faster than the worst case architectures for each matrix, showing the benefits of instance specific design for SpMV.

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Proceedings of the 2016 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays

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