Optimized Distributed Work-Stealing

Work-stealing is a popular approach for dynamic load balancing of task-parallel programs. However, as has been widely studied, the use of classical work-stealing algorithms on massively parallel and distributed supercomputers introduces several performance issues. One such issue is the overhead of failed steals (communicating with a victim that has no work), which is far more severe in the distributed context than within a single SMP node. Due to the cost of inter-node communication, it is critical to reduce the number of failed steals in a distributed context. Prior work has demonstrated that load-aware victim processor selection can reduce the number of failed steals, but it cannot eliminate the failed steals completely.In this paper, we present two different load-aware implementations of distributed work-stealing algorithm in HabaneroUPC++ PGAS library - BaselineWS and SuccessOnlyWS. BaselineWS follows prior work in implementing a distributed work-stealing strategy. SuccessOnlyWS implements a novel distributed work-stealing strategy that completely eliminate inter-node failed attempts by introducing a new policy for moving work from busy to idle processors. This strategy also avoids querying the same processor multiple times with failed steals. We evaluate both BaselineWS and SuccessOnlyWS by using up to 12288 cores of Edison, a CRAY-XC30 supercomputer and by using dynamic irregular applications, as exemplified by the UTS and NQueens benchmarks. We demonstrate that SuccessOnlyWS provides performance improvements up to 7% over BaselineWS.