When Homogeneous becomes Heterogeneous -- Wearout Aware Task Scheduling for Streaming Applications

Abstract

Recent trends in process technology suggest the need to monitor transistor wear-out in future processes. Because of withindie variation and the different computations being run on each core in a multi-core chip, this wear-out causes further imbalance to initial core frequencies as time progresses. Furthermore, manufacturing defects mean that cache sizes can vary between cores, adding further imbalance to a system. If we allow different cores to independently control their operating frequency we can achieve the best possible performance for their part of the die. Other parts of the system with slowly degrading performance can include interconnects and Flash-based file caches. In this paper we first explain how conventionally homogeneous multi-core processors can become heterogeneous over time. We discuss possible operating system based solutions to maximize the performance of a system as it wears out and present illustrative theoretical results based on linear programming. We demonstrate that for a class of streaming applications, an intelligent scheduling scheme recovers a significant amount of performance lost through wear-out. We advocate the need for multiple accurate performance measurements for effective scheduling in a wearout-aware multicore chip