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PASTA: A Parallel Sparse Tensor Algorithm Benchmark Suite

Authors :
Li, Jiajia
Ma, Yuchen
Wu, Xiaolong
Li, Ang
Barker, Kevin
Li, Jiajia
Ma, Yuchen
Wu, Xiaolong
Li, Ang
Barker, Kevin
Publication Year :
2019

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 https://gitlab.com/tensorworld/pasta.

Details

Database :
OAIster
Publication Type :
Electronic Resource
Accession number :
edsoai.on1106330783
Document Type :
Electronic Resource