Multi-task Scheduling for PIM-based Heterogeneous Computing System

Abstract

Processing-in-Memory (PIM) or Near-Data Processing has been recognized as the most potential solution to resolve the ever-aggravating memory wall especially as the thrive of memory-intensive scale-out workloads such as graph computing and data analytics. However, when the future computing system becomes more and more likely to adopt PIM architectures as a type of the storage and processing component, there is a lack of literature and research work on the general scheduling framework with the emerging heterogeneous system except for some ad-hoc task partitioning methods with specialized PIM designs. This work is the first to propose a formalized model to quantitatively describe the multi-task scheduling problem in PIM+CPU platform without loss of generality, and also an optimized task mapping-and-scheduling algorithm to boost the hardware utility for these novel heterogeneous systems. The proposed scheduling framework is fully aware of the data access bandwidth and processing capability distinction between the CPU and PIM devices, and also the implications of task mapping on the bandwidth contention, data communication intensity and hardware utility for the concurrent workloads. Experimental results show that, compared to the traditional scheduling algorithm for heterogeneous system, the proposed method is able to improve the system performance by over 10% and the energy efficiency by almost 10% for multi-core scale-out applications.