Data-Driven Techniques to Overcome Workload Disparity

General-purpose computing on graphics processing unit (GPGPU) architectures rely on data locality and regular computation to leverage parallel resources to achieve performance benefits over multi-core systems. Current GPUs often cannot effectively accommodate irregular algorithms and non-uniform memory accesses. Irregular execution corrupts the efficiency of the rigid GPU groups of threads that lead to idle execution execution and workload disparity in the GPU architecture. While hardware aspects of branch divergence, local memory bank conflicts, and non-coalesced memory accesses cause workload disparity, the greater issue is the lost of execution potential from other GPU workgroups. This paper sets out to characterize the workload disparity and toexplore data-driven models for executing irregular computations on GPU architectures.