Efficient Lossless Compression of Scientific Floating-Point Data on CPUs and GPUs
Noushin Azami, Alex Fallin, Martin Burtscher
Abstract
The amount of scientific data being produced, transferred, and processed increases rapidly. Whereas GPUs have made faster processing possible, storage limitations and slow data transfers remain key bottlenecks. Data compression can help, but only if it does not create a new bottleneck. This paper presents four new lossless compression algorithms for single- and double-precision data that compress well and are fast even though they are fully compatible between CPUs and GPUs. Averaged over many SDRBench inputs, our implementations outperform most of the 18 compressors from the literature we compare to in compression ratio, compression throughput, and decompression throughput. Moreover, they outperform all of them in either throughput or compression ratio on the two CPUs and two GPUs we used for evaluation. For example, on an RTX 4090 GPU, our fastest code compresses and decompresses at over 500 GB/s while delivering one of the highest compression ratios.