Barrier-Aware Warp Scheduling for Throughput Processors


Y. Liu, et al., “Barrier-Aware Warp Scheduling for Throughput Processors,” in Proceedings of the 2016 International Conference on Supercomputing, 2016, pp. 42.
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Parallel GPGPU applications rely on barrier synchronization to align thread block activity. Few prior work has studied and characterized barrier synchronization within a thread block and its impact on performance. In this paper, we find that barriers cause substantial stall cycles in barrier-intensive GPGPU applications although GPGPUs employ lightweight hardware-support barriers. To help investigate the reasons, we define the execution between two adjacent barriers of a thread block as a warp-phase. We find that the execution progress within a warp-phase varies dramatically across warps, which we call warp-phase-divergence. While warp-phasedivergence may result from execution time disparity among warps due to differences in application code or input, and/or shared resource contention, we also pinpoint that warp-phase-divergence may result from warp scheduling.

To mitigate barrier induced stall cycle inefficiency, we propose barrier-aware warp scheduling (BAWS). It combines two techniques to improve the performance of barrier-intensive GPGPU applications. The first technique, most-waiting-first (MWF), assigns a higher scheduling priority to the warps of a thread block that has a larger number of warps waiting at a barrier. The second technique, critical-fetch-first (CFF), fetches instructions from the warp to be issued by MWF in the next cycle. To evaluate the efficiency of BAWS, we consider 13 barrier-intensive GPGPU applications, and we report that BAWS speeds up performance by 17% and 9% on average (and up to 35% and 30%) over loosely-round-robin (LRR) and greedy-then-oldest (GTO) warp scheduling, respectively. We compare BAWS against recent concurrent work SAWS, finding that BAWS outperforms SAWS by 7% on average and up to 27%. For non-barrier-intensive workloads, we demonstrate that BAWS is performance-neutral compared to GTO and SAWS, while improving performance by 5.7% on average (and up to 22%) compared to LRR. BAWS’ hardware co

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Last updated on 05/31/2019