Many scientific programs produce large amounts of floating-point data that are saved for later use. To minimize the storage requirement, it is worthwhile to compress such data as much as possible. However, existing algorithms tend to com-press floating-point data relatively poorly. As a remedy, we have developed FPcrush, a tool that automatically synthesizes an optimized compressor for each given input. The synthesized algorithms are lossless and parallelized using OpenMP. This paper describes how FPcrush is able to perform this synthesis in real-time, i.e., even when accounting for the synthesis overhead, it compresses the 16 tested real-world single- and double-precision data files more quickly than parallel bzip2. Decompression is an order of magnitude faster and exceeds the throughput of multi-core implementations of bzip2, gzip, and FPC. On all but two of the tested files, as well as on average, the customized algorithms deliver higher compression ratios than the other three tools.

Due to their high peak performance and energy efficiency, massively parallel accelerators such as GPUs are quickly spreading in high-performance computing, where large amounts of floating-point data are processed, transferred, and stored. Such environments can greatly benefit from data compression if done sufficiently quickly. Unfortunately, most conventional compression algorithms are unsuitable for highly parallel execution. In fact, it is generally unknown how to design good compression algorithms for massively parallel systems. To remedy this situation, we study 138,240 lossless compression algorithms for single-and double-precision floating-point values that are built exclusively from easily parallelizable components. We analyze the best of these algorithms, explain why they compress well, and derive the Massively Parallel Compression (MPC) algorithm from them. This novel algorithm requires almost no internal state, achieves heretofore unreached compression ratios on several data sets, and roughly matches the best CPU-based algorithms in compression ratio while outperforming them by one to two orders of magnitude in throughput.