HPC-MixPBench: An HPC Benchmark Suite for Mixed-Precision Analysis
Konstantinos Parasyris, Ignacio Laguna, Harshitha Menon, Markus Schordan, Daniel Osei-Kuffuor, Giorgis Georgakoudis, Michael O. Lam, Tristan Vanderbruggen · 2020
With the increasing interest in applying approximate computing to HPC applications, representative benchmarks are needed to evaluate and compare various approximate computing algorithms and programming frameworks. To this end, we propose HPC-MixPBench, a benchmark suite consisting of a representative set of kernels and benchmarks that are widely used in HPC domain. HPC-MixPBench has a test harness framework where different tools can be plugged in and evaluated on the set of benchmarks. We demonstrate the effectiveness of our benchmark suite by evaluating several mixed-precision algorithms implemented in FloatSmith, a tool for floating-point mixed-precision approximation analysis. We report several insights about the mixed-precision algorithms that we compare, which we expect can help users of these methods choose the right method for their workload. We envision that this benchmark suite will evolve into a standard set of HPC benchmarks for comparing different approximate computing techniques.