PASTA: a parallel sparse tensor algorithm benchmark suite

Jiajia Li, Yuchen Ma, Xiaolong Wu, Ang Li, Kevin J. Barker

Abstract: 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 at https://gitlab.com/tensorworld/pasta , under version 0.1.0.