Automated Dynamic Analysis of CUDA Programs

Recent increases in the programmability and performance of GPUs have led to a surge of interest in utilizing them for general-purpose computations. Tools such as NVIDIA's Cuda allow programmers to use a C-like language to code algorithms for execution on the GPU. Unfortunately, parallel programs are prone to subtle correctness and performance bugs, and Cuda tool support for solving these remains a work in progress. As a first step towards addressing these problems, the authors present an automated analysis technique for finding two specific classes of bugs in Cuda programs: race conditions, which impact program correctness, and shared memory bank conflicts, which impact program performance.