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Title: Composition of Algorithmic Building Blocks in Template Task Graphs
In 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.  more » « less
Award ID(s):
1931347
PAR ID:
10489589
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
IEEE
Date Published:
Journal Name:
2022 IEEE/ACM Parallel Applications Workshop: Alternatives To MPI+X (PAW-ATM)
ISBN:
978-1-6654-5410-0
Page Range / eLocation ID:
26 to 38
Format(s):
Medium: X
Location:
Dallas, TX, USA
Sponsoring Org:
National Science Foundation
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