Parallel Computational Reconfiguration Based on a PGAS Model Parallel Computational Reconfiguration Based on a PGAS Model

In order to improve the resource utilization of clusters and supercomputers and thus deliver application results to users faster, it is essential for a job scheduler to expand and shrink parallel computations flexibly. In order to enable the flexible job scheduling, the parallel computations have to be reconfigurable. With this motivation, this paper proposes, implements and evaluates DMI, a global-view-based PGAS framework that enables easy programming of reconfigurable and high-performance parallel iterative computations. DMI provides programming interfaces with which a programmer can program the reconfiguration easily with a global-view. Our performance evaluations showed that DMI can efficiently adapt the parallelism of long-running parallel iterative computations, such as a real-world finite element method and large-scale iterative graph search, to the dynamic increase and decrease of available resources through the reconfiguration.

In order to improve the resource utilization of clusters and supercomputers and thus deliver application results to users faster, it is essential for a job scheduler to expand and shrink parallel computations flexibly. In order to enable the flexible job scheduling, the parallel computations have to be reconfigurable. With this motivation, this paper proposes, implements and evaluates DMI, a global-view-based PGAS framework that enables easy programming of reconfigurable and high-performance parallel iterative computations. DMI provides programming interfaces with which a programmer can program the reconfiguration easily with a global-view. Our performance evaluations showed that DMI can efficiently adapt the parallelism of long-running parallel iterative computations, such as a real-world finite element method and large-scale iterative graph search, to the dynamic increase and decrease of available resources through the reconfiguration.