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Feature-based SpMV Performance Analysis on Contemporary Devices

Authors: Mpakos, Panagiotis; Galanopoulos, Dimitrios; Anastasiadis, Petros; Papadopoulou, Nikela; Koziris, Nectarios; Goumas, Georgios;

Feature-based SpMV Performance Analysis on Contemporary Devices

Abstract

The SpMV kernel is characterized by high performance variation per input matrix and computing platform. While GPUs were considered State-of-the-Art for SpMV, with the emergence of advanced multicore CPUs and low-power FPGA accelerators, we need to revisit its performance and energy efficiency. This paper provides a high-level SpMV performance analysis based on structural features of matrices related to common bottlenecks of memory-bandwidth intensity, low ILP, load imbalance and memory latency overheads. Towards this, we create a wide artificial matrix dataset that spans these features and study the performance of different storage formats in nine modern HPC platforms; five CPUs, three GPUs and an FPGA. After validating our proposed methodology using real-world matrices, we analyze our extensive experimental results and draw key insights on the competitiveness of different target architectures for SpMV and the impact of each feature/bottleneck on its performance.

to appear at IPDPS'23

Keywords

FOS: Computer and information sciences, Computer Science - Distributed, Parallel, and Cluster Computing, Distributed, Parallel, and Cluster Computing (cs.DC)

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