NDLI: A data locality-aware design framework for reconfigurable sparse matrix-vector multiplication kernel

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A data locality-aware design framework for reconfigurable sparse matrix-vector multiplication kernel

Content Provider	ACM Digital Library
Author	Chen, Yiran Li, Hai Wang, Yandan Li, Sicheng Wen, Wujie Wang, Yu
Abstract	Sparse matrix-vector multiplication (SpMV) is an important computational kernel in many applications. For performance improvement, software libraries designated for SpMV computation have been introduced, e.g., MKL library for CPUs and cuSPARSE library for GPUs. However, the computational throughput of these libraries is far below the peak floating-point performance offered by hardware platforms, because the efficiency of SpMV kernel is greatly constrained by the limited memory bandwidth and irregular data access patterns. In this work, we propose a data locality-aware design framework for FPGA-based SpMV acceleration. We first include the hardware constraints in sparse matrix compression at software level to regularize the memory allocation and accesses. Moreover, a distributed architecture composed of processing elements is developed to improve the computation parallelism. We implement the reconfigurable SpMV kernel on Convey $HC-2^{ex}$ and conduct the evaluation by using the University of Florida sparse matrix collection. The experiments demonstrate an average computational efficiency of 48.2%, which is a lot better than those of CPU and GPU implementations. Our FPGA-based kernel has a comparable runtime as GPU, and achieves 2.1x reduction than CPU. Moreover, our design obtains substantial saving in energy consumption, say, 9.3x and 5.6x better than the implementations on CPU and GPU, respectively.
Starting Page	1
Ending Page	6
Page Count	6
File Format	PDF
ISBN	9781450344661
DOI	10.1145/2966986.2966987
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-11-07
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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