A Stall-Aware Warp Scheduling for Dynamically Optimizing Thread-level Parallelism in GPGPUs
Yulong Yu, Weijun Xiao, Xubin He, He Guo, Yuxin Wang, Xin Chen · 2015
General-Purpose Graphic Processing Units (GPGPU) have been widely used in high performance computing as application accelerators due to their massive parallelism and high throughput. A GPGPU generally contains two layers of schedulers, a cooperative-thread-array (CTA) scheduler and a warp scheduler, which administer the thread level parallelism (TLP). Previous research shows the maximized TLP does not always deliver the optimal performance. Unfortunately, existing warp scheduling schemes do not optimize TLP at runtime, which is impossible to fit various access patterns for diverse applications. Dynamic TLP optimization in the warp scheduler remains a challenge to exploit the GPGPU highly-parallel compute power.