Characterization of data compression across CPU platforms and accelerators

Abstract

The ever increasing amount of generated data makes it more and more beneficial to utilize compression to trade computations for data movement and reduced storage requirements. Lately, dedicated accelerators have been introduced to offload compression tasks from the main processor. However, research is lacking when it comes to the system costs for incorporating compression. This is especially true for the influence of the CPU platform and accelerators on the compression. This work will show that for general-purpose lossless compression algorithms following can be recommended: (1) snappy for high throughput, but low compression ratio; (2) zstandard level 2 for moderate throughput and compression ratio; (3) xz level 5 for low throughput, but high compression ratio. And it will show that the selected platforms (ARM, IBM or Intel) have no influence on the algorithm's performance. Furthermore, it will show that the accelerator's zlib implementation achieves a comparable compression ratio as zlib level 2 on a CPU, while having up to (Figure presented.) the throughput and utilizing over 80% less CPU resources. This suggests that the overhead of offloading compression is limited but present. Overall, this work will allow system designers to identify deployment opportunities for compression while considering integration constraints.