Optimizing Compression Schemes for Parallel Sparse Tensor Algebra

This paper studies compression techniques for parallel in-memory sparse tensor algebra. Although one might hope that sufficiently simple compression schemes would generally improve performance by decreasing memory traffic when the computation is memory-bound, we find that applying existing simple compression schemes can lead to performance loss due to the additional computational overhead. To resolve this issue, we introduce a novel algorithm called byte-opt, an optimized version of the byte format from the Ligra + graph-processing framework [1] that saves space without sacrificing performance. The byte-opt format takes advantage of per-row structure to speed up decoding without changing the underlying representation from byte.