Automatically Detecting Heterogeneous Bugs in High-Performance Computing Scientific Software

TL;DR

This paper introduces HeteroBugDetect, an automated tool that detects platform-dependent heterogeneous bugs in HPC scientific software, improving reliability across diverse hardware configurations.

Contribution

It presents a novel combination of NLP, fuzzing, and differential testing to identify bugs specific to heterogeneous computing platforms in scientific applications.

Findings

Detected multiple heterogeneous bugs in LAMMPS

Enhanced reliability of scientific software across HPC environments

Demonstrated effectiveness of combined testing techniques

Abstract

Scientific advancements rely on high-performance computing (HPC) applications that model real-world phenomena through simulations. These applications process vast amounts of data on specialized accelerators (eg., GPUs) using special libraries. Heterogeneous bugs occur in these applications when managing data movement across different platforms, such as CPUs and GPUs, leading to divergent behavior when using heterogeneous platforms compared to using only CPUs. Existing software testing techniques often fail to detect such bugs because either they do not account for platform-specific characteristics or target specific platforms. To address this problem, we present HeteroBugDetect, an automated approach to detect platform-dependent heterogeneous bugs in HPC scientific applications. HeteroBugDetect combines natural-language processing, off-target testing, custom fuzzing, and differential testing to provide an end-to-end solution for detecting platform-specific bugs in scientific applications. We evaluate HeteroBugDetect on LAMMPS, a molecular dynamics simulator, where it detected multiple heterogeneous bugs, enhancing its reliability across diverse HPC environments.