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

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. The high reliability of this PIM system (ensuring that the system operates normally for long periods of high performance) is particularly important in the era of big data. However, existing aging-award task scheduling algorithms for heterogeneous systems do not consider hardware characteristics (memory interference etc.) in PIM+CPU system, if it is directly applied in the PIM + CPU system, a better optimization effect cannot be achieved. Therefore, we built an aging reliability model for PIM + CPU heterogeneous systems and proposed an MTTF-based task scheduling algorithm, PIM-MTTF, to balance the MTTF of the entire system. Experimental results show that, compared to the traditional scheduling algorithm for heterogeneous system, the proposed method is able to reduce MTTF variation over 9.1% on average and improvement of the system performance by 13.1% on average for PIM+CPU system.