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Fengguang Song, Asim YarKhan, and Jack Dongarra (2009)

Dynamic Task Scheduling for Linear Algebra Algorithms on Distributed-Memory Multicore Systems

In: SC’09 The International Conference for High Performance Computing, Networking, Storage and Analysis, Portland, OR.

Multicore systems have increasingly gained importance in both shared-memory and distributed-memory environments. This paper presents a dynamic task scheduling approach to executing dense linear algebra algorithms on multicore systems (either shared- or distributed-memory). We use a task-based library to replace the existing linear algebra sub- routines such as PBLAS to transparently provide the same interface and computational function as the ScaLAPACK library. Linear algebra programs are written with the task- based library and executed by a dynamic runtime system. We mainly focus our runtime system design on the met- ric of performance scalability. We propose an algorithm to solve data dependences without process cooperation in a dis- tributed manner. We have implemented the runtime system and applied it to three linear algebra algorithms: Cholesky factorization, LU factorization, and QR factorization. Our experiments on both shared-memory machines (16-core In- tel Tigerton, 32-core IBM Power6) and distributed-memory machines (Cray XT4 using 1024 cores) demonstrate that our runtime system is able to achieve good scalability. Further- more, we provide analytical analysis to show why the tiled algorithms are scalable and the expected execution time.

Preprint published as Linear Algebra Working Note (LAWN) 221. Accepted
by Asim YarKhan last modified 2009-08-27 06:18
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