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Current high-performance computer systems used for scientific computingtypically combine shared memory computational nodes in a distributed memoryenvironment. Extracting high performance from these complex systems requirestailored approaches. Task based parallel programming has been successful bothin simplifying the programming and in exploiting the available hardwareparallelism for shared memory systems. In this paper we focus on how to extendtask parallel programming to distributed memory systems. We use a hierarchicaldecomposition of tasks and data in order to accommodate the different levels ofhardware. We test the proposed programming model on two different applications,a Cholesky factorization, and a solver for the Shallow Water Equations. We alsocompare the performance of our implementation with that of other frameworks fordistributed task parallel programming, and show that it is competitive.

DuctTeip: An efficient programming model for distributed task based parallel computing