Accelerating Lbm And Lqcd Application Kernels By In-Memory Processing

Processing-in-memory architectures promise increased computing performance at decreased costs in energy, as the physical proximity of the compute pipelines to the data store eliminates overheads for data transport. We assess the overall performance impact using a recently introduced architecture of that type, called the Active Memory Cube, for two representative scientific applications. Precise performance results for performance critical kernels are obtained using cycle-accurate simulations. We provide an overall performance estimate using performance models.