GSpTC: High-Performance Sparse Tensor Contraction on CPU-GPU Heterogeneous Systems

Guoqing Xiao, Chuanghui Yin, Yuedan Chen, Mingxing Duan, Kenli Li

Abstract: Many fields of scientific simulation, such as chem-istry and condensed matter physics, are increasingly eschewing dense tensor contraction in favor of sparse tensor contraction. In this work, we centre around binary sparse tensor contraction (SpTC) which has the challenges of index matching and accumulation. To address and alleviate the above difficulties, we present GSpTC, an efficient element-wise SpTC framework on CPU-GPU heterogeneous systems. GSpTC first introduces a fine-grained index partition based on element-wise tensor con-traction, which facilitates efficient use of the parallel computing power of GPUs. In particular, GSpTC leverages multi-threading parallelism on GPUs for the contraction phase and merge phase, which greatly accelerates the computation phase in sparse tensor contraction. Through evaluation of five datasets, we find that the proposed GSpTC achieves an average performance improvement of 74.32% compared to the previous state-of-the-art Sparta framework.