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Section: New Results

Fast and Accurate Simulation of Multithreaded Sparse Linear Algebra Solvers

Participants : Luka Stanisic [PhD, Inria, Mescal] , Arnaud Legrand [CNRS, Inria, Mescal] , Emmanuel Agullo [Inria, HiePacs] , Alfredo Buttari [CNRS, IRIT, Toulouse] , Florent Lopez [CNRS, IRIT, Toulouse] , Brice Videau.

The ever growing complexity and scale of parallel architectures imposes to rewrite classical monolithic HPC scientific applications and libraries as their portability and performance optimization only comes at a prohibitive cost. There is thus a recent and general trend in using instead a modular approach where numerical algorithms are written at a high level independently of the hardware architecture as Directed Acyclic Graphs (DAG) of tasks. A task-based runtime system then dynamically schedules the resulting DAG on the different computing resources, automatically taking care of data movement and taking into account the possible speed heterogeneity and variability. Evaluating the performance of such complex and dynamic systems is extremely challenging especially for irregular codes. In this article, we explain how we crafted a faithful simulation, both in terms of performance and memory usage, of the behavior of qr_mumps , a fully-featured sparse linear algebra library, on multi-core architectures. In our approach, the target high-end machines are calibrated only once to derive sound performance models. These models can then be used at will to quickly predict and study in a reproducible way the performance of such irregular and resource-demanding applications using solely a commodity laptop.

This work is part of the Luka Stanisic's thesis. Luka stanisic was coadvised by Arnaud Legrand, Brice Videau and Jean-François Méhaut. This thesis was defended in November 2015. Luka Stanisic currently holds a postdoc position at Inria Bordeaux in the Storm and HiePacs teams. This work was published in the ICPADS'2015 conference [18] .