Selective off-loading to Memory: Task Partitioning and Mapping for PIM-enabled Heterogeneous Systems

Abstract

Processing-in-Memory (PIM) is returning as a promising solution to address the issue of memory wall as computing systems gradually step into the big data era. Researchers continually proposed various PIM architecture combined with novel memory device or 3D integration technology, but it is still a lack of universal task scheduling method in terms of the new heterogeneous platform. In this paper, we propose a formalized model to quantify the performance and energy of the PIM+CPU heterogeneous parallel system. In addition, we are the first to build a task partitioning and mapping framework to exploit different PIM engines. In this framework, an application is divided into subtasks and mapped onto appropriate execution units based on the proposed PIM-oriented Earliest-Finish-Time (PEFT) algorithm to maximize the performance gains brought by PIM. Experimental evaluations show our PIM-aware framework significantly improves the system performance compared to conventional processor architectures.