发表机构
Georgia Institute of Technology; Microsoft; NVIDIA（佐治亚理工学院; 微软; 英伟达）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出GMSBench基准，含149个CUDA测试覆盖空间、时间和并发错误，标准化评估GPU内存安全机制，并通过Compute Sanitizer验证其有效性。
AI 中文摘要
随着GPU在高性能计算和机器学习中日益不可或缺，确保GPU程序的内存安全对于可靠和安全执行变得至关重要。然而，由于缺乏全面且标准化的基准，评估GPU内存安全技术仍具挑战性。本文提出GMSBench，一个GPU内存安全基准，旨在评估不同GPU内存空间和执行场景下的广泛内存安全违规。GMSBench包含149个自包含的CUDA测试，涵盖空间、时间和并发错误。该套件为GPU内存安全机制的评估和比较分析提供了标准化基础，并有助于揭示其检测覆盖中的空白。我们通过评估Compute Sanitizer（一种广泛使用的GPU内存错误检测工具）在多种GPU架构上的表现，展示了GMSBench的实用性。
英文摘要
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.
Comments5 pages, 1 figure, 2 tables
