Improved data transfer efficiency for scale‐out heterogeneous workloads using on‐the‐fly I/O link compression

Abstract

Graphics processing units (GPUs) are unarguably vital to keep up with the perpetually growing demand for compute capacity of data-intensive applications. However, the overhead of transferring data between host and GPU memory is already a major limiting factor on the single-node level. The situation intensifies in scale-out scenarios, where data movement is becoming even more expensive. By augmenting the CloudCL framework with 842-based compression facilities, this article demonstrates that transparent on-the-fly I/O link compression can yield performance improvements between 1.11× and 2.07× across tested scale-out GPU workloads.