2021/07/12 by Charitha Saumya, Saumya, Charitha, Kirshanthan Sundararajah +3 · 2 citations
Computer Science · #Embedded Systems Design Techniques #FOS: Computer and information sciences #Parallel Computing and Optimization Techniques #Performance (cs.PF) #Programming Languages (cs.PL) #Security and Verification in Computing
paper · pdf · doi:10.48550/arxiv.2107.05681
openalex publication_date 2021/07/12 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
GPGPUs use the Single-Instruction-Multiple-Thread (SIMT) execution model where a group of threads-wavefront or warp-execute instructions in lockstep. When threads in a group encounter a branching instruction, not all threads in the group take the same path, a phenomenon known as control-flow divergence. The control-flow divergence causes performance degradation because both paths of the branch must be executed one after the other. Prior research has primarily addressed this issue through architectural modifications. We observe that certain GPGPU kernels with control-flow divergence have similar control-flow structures with similar instructions on both sides of a branch. This structure can be exploited to reduce control-flow divergence by melding the two sides of the branch allowing threads to reconverge early, reducing divergence. In this work, we present DARM, a compiler analysis and transformation framework that can meld divergent control-flow structures with similar instruction sequences. We show that DARM can reduce the performance degradation from control-flow divergence.