RedSan: A Redundant Memory Instruction Sanitizer for GPU Programs

CUDA is the de facto programming model for GPUs, which is widely used in the domains of HPC and AI. To obtain bare-metal performance, vendors and academia develop various profiling tools to guide optimization. However, most existing tools focus on hotspot analysis with limited capabilities in identifying actionable opportunities. To complement existing tools, we present RedSan, a novel profiling tool that leverages binary instrumentation to identify redundant instructions in fully optimized CUDA programs. Guided by RedSan, we are able to optimize programs such as PolybenchGPU, Rodinia, PASTA, DARKNET, and LULESH, yielding up to a 6.27 × speedup and 3.00 × reduction in memory instructions.