Towards Standardized Evaluation of GPU Memory Safety with GMSBench

2026-09-08

AI Summary

本文通过开发GMSBench解决GPU程序内存安全性评估缺乏标准化基准的问题，该工具包含149个CUDA测试用例，用于检测多种内存错误。

Abstract

As GPUs become increasingly integral to high-performance computing and machine learning, ensuring memory safety in GPU programs has become crucial for reliable and secure execution. However, evaluating GPU memory safety techniques remains challenging due to the lack of comprehensive and standardized benchmarks. In this paper, we present GMSBench, a GPU memory safety benchmark designed to evaluate a broad range of memory safety violations across different GPU memory spaces and execution scenarios. GMSBench comprises 149 self-contained CUDA tests spanning spatial, temporal, and concurrency errors. The suite provides a standardized foundation for the evaluation and comparative analysis of GPU memory safety mechanisms and helps expose gaps in their detection coverage. We demonstrate the utility of GMSBench by evaluating Compute Sanitizer, a widely used GPU memory error detection tool across multiple GPU architectures.

Problem

• GPU memory safety
• evaluation
• benchmarks
• memory errors

Innovation

• GMSBench
• memory safety
• CUDA tests
• Compute Sanitizer
• benchmark