Genetic Improvement of GPU Code

As the programming stack and tool support for GPU have matured, GPUs have become accessible to programmers who often lack domain-specific knowledge of the underlying architecture and fail to fully leverage the GPU's computation power. This paper presents GEVO (Gpu EVOlution), a tool for automatically tuning the performance of GPU kernels in the LLVM representation to meet desired criteria. GEVO uses population-based search to find edits to programs compiled to LLVM-IR that improve performance on desired criteria and retain required functionality. GEVO extends earlier GI work by operating directly on the LLVM-IR without custom representations or other manual interventions. We demonstrate that GEVO improves runtime on NVIDIA Tesla P100 for many programs in the Rodinia benchmark suite and a supervised machine learning code, ThunderSVM. For the Rodinia benchmark, GEVO improves GPU kernel runtime performance by an average of 13.87% and as much as 43% over the fully compiler-optimized baseline. If the kernel output accuracy is relaxed to tolerate 1% error, GEVO can find kernel variants that outperform the baseline version by an average of 15.47%. For ThunderSVM, GEVO reduces entire model training time by 50% and 24.8%, for MNIST handwriting recognition dataset and a9a income prediction, where the accuracy of trained model are improved by 0.17% and 0.04% respectively.