Binsparse: A Specification for Cross-Platform Storage of Sparse Matrices and Tensors
Benjamin Brock, Willow Ahrens, Hameer Abbasi, Timothy A. Davis, Juni Kim, James Kitchen, Spencer Patty, Isaac Virshup, Erik Welch

TL;DR
Binsparse introduces a cross-platform, binary storage format for sparse matrices and tensors that significantly reduces file size and improves read/write performance compared to existing ASCII-based formats.
Contribution
It provides a modular, embeddable binary format specification with multiple reference implementations, addressing the lack of a universal binary sparse storage standard.
Findings
File size reduced by up to 7.5x with compression.
Read speed improved by up to 26.5x over Matrix Market.
Write speed increased by up to 31x.
Abstract
Sparse matrices and tensors are ubiquitous throughout multiple subfields of computing. The widespread usage of sparse data has inspired many in-memory and on-disk storage formats, but the only widely adopted storage specifications are the Matrix Market and FROSTT file formats, which both use ASCII text. Due to the inefficiency of text storage, these files typically have larger file sizes and longer parsing times than binary storage formats, which directly store an in-memory representation to disk. This can be a major bottleneck; since sparse computation is often bandwidth-bound, the cost of loading or storing a matrix to disk often exceeds the cost of performing a sparse computation. While it is common practice for practitioners to develop their own, custom, non-portable binary formats for high-performance sparse matrix storage, there is currently no cross-platform binary sparse matrix…
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Taxonomy
TopicsParallel Computing and Optimization Techniques · Tensor decomposition and applications
