Efficient task-mapping of parallel applications using a space-filling curve

Abstract

Improving the communication performance of parallel programs is an important but difcult problem in a large-scale distributed memory-based cluster. Efforts to improve parallel scalability often face severe huddles in managing communication overheads. This paper proposes a framework of a space-fling curve(SFC)-based task-remapping for communication intensive parallel applications. An SFC-based mapping, when applied for task-mapping of parallel applications preserves locality in terms of communications and produce a less fragmented task-mapping, reducing communication overheads. The framework also provides tools for performance analysis to see if the proposed task-mapping is appropriate for a given application running on a target system. It further develops a binary classifer as a predictor to decide whether or not to apply the proposed mapping before run-time. We evaluate the framework with three communication intensive applications in Cartesian coordinates: P3DFFT solver and Channel code using 2D domain decomposition model, and Poisson solver using 3D domain decomposition. The evaluation is conducted on a large-scale cluster system of fat-tree topology with up to 1,024 compute nodes. The proposed task-mapping achieves the overall performance improvement ranging from ∼30% to ∼66% over the baseline approach depending on the workloads. Also, when used in combination with the binary classifer-based predictor, it achieves the expected performance gains from 4% to 8%.