SAWS: Synchronization aware GPGPU warp scheduling for multiple independent warp schedulers

General-purpose computing on Graphics Processing Units (GPGPUs) became increasingly popular for a wide range of applications beyond traditional graphic rendering workloads. GPGPU exploits parallelism in applications via multithreading to hide memory latencies, and handles control complexity by barrier synchronizations. Warp scheduling algorithms have been optimized to increase memory latency hiding capability, improve cache behavior, or alleviate branch divergence. To date, there is no scheduler that accounts for the synchronization behavior among warps under the presence of multiple warp schedulers. In this paper, we develop a warp scheduling algorithm that is synchronization aware. The key observation is that excessive stall cycles may be introduced due to synchronizing warps residing in different warp schedulers. We propose that schedulers coordinate with each other to avoid warps from being blocked on a barrier for overly long. Such coordination will dynamically reorder the execution sequence of warps so that they are issued in proximity when a barrier is encountered. Performance evaluations demonstrate that our proposed coordinated schedulers can improve the performance of synchronization-rich benchmarks by 10% on average when compared to the state-of-the-art non-coordinating schedulers.