Varity: Quantifying Floating-Point Variations in HPC Systems Through Randomized Testing

Abstract

Floating-point arithmetic can be confusing and it is sometimes misunderstood by programmers. While numerical reproducibility is desirable in HPC, it is often unachievable due to the different ways compilers treat floating-point arithmetic and generate code around it. This reproducibility problem is exacerbated in heterogeneous HPC systems where code can be executed on different floating-point hardware, e.g., a host and a device architecture, producing in some situations different numerical results. We present VARITY, a tool to quantify floatingpoint variations in heterogeneous HPC systems. Our approach generates random test programs for multiple architectures (host and device) using the compilers that are available in the system. Using differential testing, it compares floating-point results and identifies unexpected variations in the program results. The results can guide programmers in choosing the compilers that produce the most similar results in a system, which is useful when numerical reproducibility is critical. By running 50,000 experiments with Varity on a system with IBM POWER9 CPUs, NVIDIA V100 GPUs, and four compilers (gcc, clang, xl, and nvcc), we identify and document several programs that produce significantly different results for a given input when different compilers or architectures are used, even when a similar optimization level is used everywhere.