Searching CUDA code autotuning spaces with hardware performance counters: data from benchmarks running on various GPU architectures.

ABSTRACT: We have developed several autotuning benchmarks in CUDA that take into account performance-relevant source-code parameters and reach near peak-performance on various GPU architectures. We have used them during the development and evaluation of a search method for tuning space proposed in [1]. With our framework Kernel Tuning Toolkit, freely available at Github, we measured computation times and hardware performance counters on several GPUs for the complete tuning spaces of five benchmarks. These data, which we provide here, might benefit research of search algorithms for the tuning spaces of GPU codes or research of relation between applied code optimization, hardware performance counters, and GPU kernels' performance. Moreover, we describe the scripts we used for robust evaluation of our

SUBMITTER: Hozzova J 

PROVIDER: S-EPMC8633859 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature