Towards green GPUs: Warp size impact analysis

Selecting the right GPU configuration can impact the overall design in many ways. One of the critical parameters in a GPU is warp size. Smaller warps come with branch divergence reduction while larger warps provide better memory coalescing. In this work we are interested in two possible design choices and their impacts on GPUs: using small warps and investing in finding new solutions to enhance coalescing or using large warps and addressing branch divergence by employing effective control-flow solutions. We also analyze warp size impact on memory coalescing and branch divergence and hence energy. We use our findings to study two machines: a GPU using small warps but equipped with excellent memory coalescing (SW+) and a GPU using large warps but employing an MIMD engine immune from control-flow costs (LW+). We conclude that SW+ provides better energy efficiency when compared to LW+.