On Optimal Kernel Size for Integrated CPU-GPUs — A Case Study

Integrated CPU-GPU architectures with a fully addressable shared memory completely eliminate any CPU-GPU data transfer overhead. Since such architectures are relatively new, it is unclear what level of interaction between the CPU and GPU attains the best energy efficiency. Too coarse grained or larger kernels with fairly low CPU - GPU interaction could cause poor utilization of the shared resources while too fine grained kernels could cause frequent interrupts of GPU computation and performance degradation. Also larger kernels require larger shared resources causing increase in area and parasitics which affect the latency sensitive CPU cores. In this paper, we show the effect of granularity on the overall system´s energy efficiency using a synthetic workload. We describe how our framework models a truly unified shared memory in integrated architectures with frequent CPU - GPU communication.