The Relentless Execution Model for Task-Uncoordinated Parallel Computation in Distributed Memory Environments
Student: Lucas A. Wilson (The University of Texas at San Antonio)
Advisor: Jeffery von Ronne (The University of Texas at San Antonio)
Abstract: This work demonstrates the feasibility of executing tightly-coupled parallel algorithms in a task-uncoordinated fashion, where that tasks do not use any explicit interprocess communication (e.g., messages, shared memory, semaphores, etc.). The proposed model achieves this through the use of dataflow program representation, and the use of a distributed, eventually-consistent key/value store to memoize intermediate values and their associated state.
In addition, this work also details a new domain-specific language called StenSAL which allows for simple description of stencil-based scientific applications. Experiments performed on the Stampede supercomputer in Austin, Texas, demonstrate the ability of task-uncoordinated parallel execution models to scale efficiently in both shared memory and distributed memory environments.
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