Opportunity for compute partitioning in pursuit of energy-efficient systems

Abstract

Performance of computing systems, from handhelds to supercomputers, is increasingly constrained by the energy consumed. A significant and increasing fraction of the energy is consumed in the movement of data. In a compute node, caches have been very effective in reducing data movement by exploiting the available data locality in programs. Program regions with poor data locality, then effect most of the data movement, and consequently consume an ever larger fraction of energy. In this paper we explore the energy efficiency opportunity of minimizing the data movement in precisely such program regions, by first imagining the possibility of compute near memory, and then partitioning the program’s execution between a compute core and the compute near memory (CnM). Due to the emergence of 3D stacked memory, a CnM implementation appears more realistic. Our focus is on evaluating the partitioning opportunity in applications and to do a limit study of systems enabled with CnM capabilities to understand and guide their architectural embodiment. We describe an automated method of analyzing the data access pattern of optimized workload binaries, via a binary-instrumentation tool called SnapCnM, to identify the beneficial program regions (loops) for CnM execution.We also perform a limit study to evaluate the impact of such partitioning over a range of parameters affecting CnM design choices. Our results show that compute partitioning a small (<10%) fraction of a workload can improve its energy efficiency from 3% (for compute-bound applications) to 27% (for memory-bound applications). From the study in this work we discuss the important aspects that help to shape the future CnM design space.