Improved data transfer efficiency for scale‐out heterogeneous workloads using on‐the‐fly I/O link compression
Concurrency and Computation: Practice and Experience(2020)
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 1.11x and 2.07x across tested scale-out GPU workloads.
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Key words
data transfer efficiency, GPU computing, I, O link compression, scale‐, out computing
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