CAWS: criticality-aware warp scheduling for GPGPU workloads

The ability to perform fast context-switching and massive multi-threading is the forte of modern GPU architectures, which have emerged as an efficient alternative to traditional chip-multiprocessors for parallel workloads. One of the main benefits of such architecture is its latency-hiding capability. However, the efficacy of GPU’s latency-hiding varies signif-icantly across GPGPU applications. To investigate this, this paper first proposes a new al-gorithm that profiles execution behavior of GPGPU applica-tions. We characterize latencies caused by various pipeline hazards, memory accesses, synchronization primitives, and the warp scheduler. Our results show that the current round-robin warp scheduler works well in overlapping various la-tency stalls with the execution of other available warps fo