PASTA: A Parallel Sparse Tensor Algorithm Benchmark Suite

2019/02/08 by Jiajia Li, Li, Jiajia, Yuchen Ma +7 · 1 citation
Computer Science · Mathematics · #Algorithms and Data Compression #Distributed #FOS: Computer and information sciences #Parallel #Parallel Computing and Optimization Techniques #Tensor decomposition and applications #and Cluster Computing (cs.DC)

paper · pdf · doi:10.48550/arxiv.1902.03317

openalex publication_date 2019/02/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

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.

Citations

• Some Mathematical Notes on Three-Mode Factor Analysis
• Analysis of Individual Differences in Multidimensional Scaling Via an N-way Generalization of “Eckart-Young” Decomposition
• Tensor Decompositions and Applications
• Unfolding Latent Tree Structures using 4th Order Tensors
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• Numerical optimization

Cited by

• CUTHERMO: Understanding GPU Memory Inefficiencies with Heat Map Profiling

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