Warp-Based Load/Store Reordering to Improve GPU Data Cache Time Predictability and Performance

Graphics Processing Units (GPUs) have great potential to improve the performance and energy efficiency for data-parallel real-time applications. However, it is very difficult to compute worst-case execution time (WCET) for current GPUs that are design for improving the average-case throughput, not for time predictability. In this paper, we propose a warp-based load/store reordering mechanism to improve the time predictability of GPU data caching without incurring much performance overhead. This mechanism can be used in conjunction with dynamic warp scheduling to achieve better performance than a pure round-robin based scheduling while enabling accurate static timing analysis to bound the worst-case GPU L1 data cache misses.