一句话：提出面向 CUDA 的内存安全检测工具 cuCatch，用编译器插桩与驱动支持低开销捕获时空违规。
CUDA、OpenCL 和 OpenACC 是 NVIDIA GPU 通用计算的主要编程方式，但同样面临 C/C++ 中常见的内存安全漏洞；GPU 执行环境具有多个不同内存空间和成千上万并发线程，且防护机制较少，直接移植 CPU 方案困难。本文提出 cuCatch，一种专为 CUDA 编程模型设计的内存安全错误检测工具。它结合优化的编译器插桩与驱动支持，实现一种可捕获空间和时间内存安全错误的新算法。在大量 GPU 应用上的实验表明，cuCatch 平均仅带来 19% 运行时开销，比当前最先进的 GPU 调试工具快数个数量级，同时具有更高的错误检测覆盖率，适合加速 GPU 应用的内存安全调试。
CUDA, OpenCL, and OpenACC are the primary means of writing general-purpose software for NVIDIA GPUs, all of which are subject to the same well-documented memory safety vulnerabilities currently plaguing software written in C and C++. One can argue that the GPU execution environment makes software development more error prone. Unlike C and C++, CUDA features multiple, distinct memory spaces to map to the GPU’s unique memory hierarchy, and a typical CUDA program has thousands of concurrently executing threads. Furthermore, the CUDA platform has fewer guardrails than CPU platforms that have been forced to incrementally adjust to a barrage of security attacks. Unfortunately, the peculiarities of the GPU make it difficult to directly port memory safety solutions from the CPU space. This paper presents cuCatch, a new memory safety error detection tool designed specifically for the CUDA programming model. cuCatch combines optimized compiler instrumentation with driver support to implement a novel algorithm for catching spatial and temporal memory safety errors with low performance overheads. Our experimental results on a wide set of GPU applications show that cuCatch incurs a 19% runtime slowdown on average, which is orders of magnitude faster than state-of-the-art debugging tools on GPUs. Moreover, our quantitative evaluation demonstrates cuCatch’s higher error detection coverage compared to prior memory safety tools. The combination of high error detection coverage and low runtime overheads makes cuCatch an ideal candidate for accelerating memory safety debugging for GPU applications.
@article{paperbot2009,
title = {cuCatch: A Debugging Tool for Efficiently Catching Memory Safety Violations in CUDA Applications},
author = {Mohamed Tarek Ibn Ziad and Sana Damani and Aamer Jaleel and Stephen W. Keckler and Mark Stephenson},
journal = {Proceedings of the ACM on Programming Languages},
volume = {7},
number = {PLDI},
year = {2023},
doi = {10.1145/3591225}
}