In-memory data compression for sparse matrices

Authors:
Orion Sky Lawlor
Affiliations:
U. Alaska Fairbanks
Venue:
IA^3 '13 Proceedings of the 3rd Workshop on Irregular Applications: Architectures and Algorithms
Year:
2013

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Abstract

We present a high performance in-memory lossless data compression scheme designed to save both memory storage and bandwidth for general sparse matrices. Because the storage hierarchy is increasingly becoming the limiting factor in overall delivered machine performance, this type of data structure compression will become increasingly important. Compared to conventional compressed sparse row (CSR) using 32-bit column indices, compressed column indices (CCI) can be over 90% smaller, yet still be decompressed at tens of gigabytes per second. We present time and space savings for 20 standard sparse matrices, on multicore CPUs and modern GPUs.