MTTF-Aware Reliability Task Scheduling for PIM-Based Heterogeneous Computing System

Abstract

Processing-in-Memory (PIM) has been recognized as the most feasible solution to resolve the ever-aggravating memory wall especially as the boom 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 unit, existing aging-award task scheduling algorithms for heterogeneous systems do not consider memory interference in PIM+CPU system, deducing an inaccurate task runtime and temperature which will over-estimate MTTF. We proposed a quantitatively formalized model for the aging reliability of PIM+CPU heterogeneous system and MTTF-ALG (a MTTF-based task scheduling algorithm) to balance the MTTF of whole system. Experimental results show that, compared to the traditional scheduling algorithm for heterogeneous system, the proposed method is able to reduce MTTF variation over 60.2% on average and the runtime by 15.3% on average for PIM+CPU system.