Do We Need a Crystal Ball for Task Migration?

For communication-intensive applications on distributed mem-ory systems, performance is bounded by remote memory accesses. Task migration is a potential candidate for reduc-ing network traffic in such applications, thereby improving performance. We seek to answer the question: can a run-time profitably predict when it is better to move the task to the data than move the data to the task? Using a simple model where local work is free and data transferred over the network is costly, we show that a best case task migration schedule can achieve up to 3.5x less total data transferred than no migration for some benchmarks. Given this obser-vation, we develop and evaluate two online task migration policies: Stream Predictor, which uses only immediate re-mote access history, and Hindsight Migrate, which tracks instruction addresses where task migration is predicted to be beneficial. These predictor policies are able to provide benefit over execution with no migration for small or mod-erate size tasks on our tested applications. 1