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 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.

中文翻译：

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跨 CPU 平台和加速器的数据压缩特性

生成的数据量不断增加，使得利用压缩来交换数据移动的计算和减少存储需求变得越来越有利。最近，引入了专用加速器来卸载主处理器的压缩任务。然而，对于合并压缩的系统成本缺乏研究。对于 CPU 平台和加速器对压缩的影响尤其如此。这项工作将表明，对于通用无损压缩算法，可以推荐以下算法：（1）snappy用于高吞吐量，但压缩比较低；(2) zstandard 2 级，用于中等吞吐量和压缩比；(3) xz 5 级用于低吞吐量但高压缩比。它将显示所选平台（ARM、IBM 或 Intel）对算法的性能没有影响。此外，它还表明，加速器的 zlib 实现在 CPU 上实现了与zlib 2 级相当的压缩比，同时具有高达吞吐量并使用了 80% 以上的 CPU 资源。这表明卸载压缩的开销是有限的但仍然存在。总体而言，这项工作将使系统设计人员能够在考虑集成约束的同时确定压缩的部署机会。