The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Workshop: The 11th International Workshop on Data Analysis and Reduction for Big Scientific Data
Authors: Brandon Alexander Burtchell and Martin Burtscher (Texas State University)
Abstract: Different compilers can generate code with notably different performance characteristics—even on the same system. Today, GPU developers have three popular options for compiling CUDA or HIP code for GPUs. First, CUDA code can be compiled by either NVCC or Clang for NVIDIA GPUs. Alternatively, AMD’s recently introduced HIP platform makes porting from CUDA to HIP relatively simple, enabling compilation for AMD and NVIDIA GPUs. This study compares the performance of 107,632 data-compression algorithms when compiling them with different compilers and running them on different GPUs from NVIDIA and AMD. We find that the relative performance of some of these codes changes significantly depending on the compiler and hardware used. For example, Clang tends to produce relatively slow compressors but relatively fast decompressors compared to NVCC and HIPCC.
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