Automated GPU Out-of-Bound Access Detectionand Prevention in a Managed Environment

GPUs have proven extremely effective at accelerating general-purpose workloads in fields from numerical simulation to deep learning and finance. However, even code written by experienced GPU programmers often offers little robustness, limiting the GPUs’ adoption in critical applications’ acceleration. Out-of-bounds array accesses are one of the most common sources of errors and vulnerabilities on GPUs and can be hard to detect and prevent due to the architectural characteristics of GPUs.This work presents an automated technique ensuring detection and protection against out-of-bounds array accesses inside CUDA GPU kernels. We compile kernels ahead-of-time, invoke them at run time using the Graal polyglot Virtual Machine and execute them on the GPU. Our technique is transparent to the user and operates on the LLVM Intermediate Representation. It adds boundary checks for array accesses based on array size knowledge, available at run time thanks to the managed execution environment, and optimizes the resulting code to minimize the impact of our modifications.We test our technique on 16 different GPU kernels extracted from common GPU workloads and show that we can prevent out-of-bounds array accesses in arbitrary GPU kernels without any statistically significant execution time overhead.