GPUDrano: Detecting Uncoalesced Accesses in GPU Programs

Graphics Processing Units (GPUs) have become widespread and popular over the past decade. Fully utilizing the parallel compute and memory resources that GPUs present remains a significant challenge, however. In this paper, we describe GPUDrano: a scalable static analysis that detects uncoalesced global memory accesses in CUDA programs. Uncoalesced global memory accesses arise when a GPU program accesses DRAM in an ill-structured way, increasing latency and energy consumption. We formalize the GPUDrano static analysis and compare it empirically against a dynamic analysis to demonstrate that false positives are rare for most programs. We implement GPUDrano in LLVM and show that it can run on GPU programs of over a thousand lines of code. GPUDrano finds 133 of the 143 uncoalesced static memory accesses in the popular Rodinia GPU benchmark suite, demonstrating the precision of our implementation. Fixing these bugs leads to real performance improvements of up to 25%.