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.
@article{arxiv.2302.04225,
title = {Feature-based SpMV Performance Analysis on Contemporary Devices},
author = {Panagiotis Mpakos and Dimitrios Galanopoulos and Petros Anastasiadis and Nikela Papadopoulou and Nectarios Koziris and Georgios Goumas},
journal= {arXiv preprint arXiv:2302.04225},
year = {2023}
}