Composition of Algorithmic Building Blocks in Template Task Graphs
DescriptionIn this paper, we explore the composition capabilities of the Template Task Graph (TTG) programming model. We show how fine-grain composition of tasks is possible in TTG between DAGs belonging to different libraries, even in a distributed setup. We illustrate the benefits of this fine-grain composition on a linear algebra operation, the matrix inversion via the Cholesky method, which consists of three operations that need to be applied in sequence.
Evaluation on a cluster of many core shows that the transparent fine-grain composition implements the complex operation without introducing unnecessary synchronizations, increasing the overlap of communication and computation, and thus improving significantly the performance of the entire composed operation.
TimeMonday, 14 November 202211:14am - 11:37am CST
Parallel Programming Languages and Models