Tensor methods have gained increasingly attention from various applications, including machine learning, quantum chemistry, healthcare analytics, social network analysis, data mining, and signal processing, to name a few. Sparse tensors and their algorithms become critical to further improve the performance of these methods and enhance the interpretability of their output. This work presents a sparse tensor algorithm benchmark suite (PASTA) for single- and multi-core CPUs. To the best of our knowledge, this is the first benchmark suite for sparse tensor world. PASTA targets on: 1) helping application users to evaluate different computer systems using its representative computational workloads; 2) providing insights to better utilize existed computer architecture and systems and inspiration for the future design. This benchmark suite is publicly released https://gitlab.com/tensorworld/pasta.
@article{arxiv.1902.03317,
title = {PASTA: A Parallel Sparse Tensor Algorithm Benchmark Suite},
author = {Jiajia Li and Yuchen Ma and Xiaolong Wu and Ang Li and Kevin Barker},
journal= {arXiv preprint arXiv:1902.03317},
year = {2019}
}