GRace: a low-overhead mechanism for detecting data races in GPU programs

In recent years, GPUs have emerged as an extremely cost-effective means for achieving high performance. Many application developers, including those with no prior parallel programming experience, are now trying to scale their applications using GPUs. While languages like CUDA and OpenCL have eased GPU programming for non-graphical applications, they are still explicitly parallel languages. All parallel programmers, particularly the novices, need tools that can help ensuring the correctness of their programs. Like any multithreaded environment, data races on GPUs can severely affect the program reliability. Thus, tool support for detecting race conditions can significantly benefit GPU application developers. Existing approaches for detecting data races on CPUs or GPUs have one or more of the following limitations: 1) being illsuited for handling non-lock synchronization primitives on GPUs; 2) lacking of scalability due to the state explosion problem; 3) reporting many false positives...

• Type: Journal
• Year: 2011
• Where: PPOPP
• Authors: Mai Zheng, Vignesh T. Ravi, Feng Qin, Gagan Agrawal
• Keywords: Distributed And Parallel Computing, Many False Positives, Parallel Programming Experience, PPOPP 2011, State Explosion Problem