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3 results on '"Julien Bigot"'

1. Optimization of the Gyroaverage operator based on Hermite interpolation

Author

Christophe Steiner, Michel Mehrenberger, Julien Bigot, Virginie Grandgirard, Guillaume Latu, T. Cartier-Michaud, Jean Roman, Fabien Rozar, Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), High-End Parallel Algorithms for Challenging Numerical Simulations (HiePACS), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institut de Recherche sur la Fusion par confinement Magnétique (IRFM), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Institut de Recherche Mathématique Avancée (IRMA), Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), TOkamaks and NUmerical Simulations (TONUS), Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)-Inria Nancy - Grand Est, Maison de la Simulation (MDLS), Centre National de la Recherche Scientifique (CNRS)-Université Paris-Saclay-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, and Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut National de Recherche en Informatique et en Automatique (Inria)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)

Subjects
FOS: Computer and information sciences, Scheme (programming language), Computer science, Computation, [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], [PHYS.MPHY]Physics [physics]/Mathematical Physics [math-ph], FOS: Physical sciences, 01 natural sciences, Plasma physics, 010305 fluids & plasmas, Operator (computer programming), Hermite interpolation, 0103 physical sciences, QA1-939, FOS: Mathematics, Code (cryptography), Optimisation, Fraction (mathematics), 0101 mathematics, computer.programming_language, T57-57.97, Applied mathematics. Quantitative methods, Computer Science - Numerical Analysis, Numerical Analysis (math.NA), Computational Physics (physics.comp-ph), Data structure, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, 010101 applied mathematics, Computer Science - Distributed, Parallel, and Cluster Computing, Product (mathematics), Hermite Interpolation, Distributed, Parallel, and Cluster Computing (cs.DC), [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], Physics - Computational Physics, Algorithm, computer, Mathematics
Abstract

Gyrokinetic modeling is appropriate for describing Tokamak plasma turbulence, and the gyroaverage operator is a cornerstone of this approach. In a gyrokinetic code, the gyroaveraging scheme needs to be accurate enough to avoid spoiling the data but also requires a low computation cost because it is applied often on the main unknown, the 5D guiding-center distribution function, and on the 3D electric potentials. In the present paper, we improve a gyroaverage scheme based on Hermite interpolation used in the Gysela code. This initial implementation represents a too large fraction of the total execution time. The gyroaverage operator has been reformulated and is now expressed as a matrix-vector product and a cache-friendly algorithm has been setup. Different techniques have been investigated to quicken the computations by more than a factor two. Description of the algorithms is given, together with an analysis of the achieved performance.

Published
2016
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2. Evaluating Kernels on Xeon Phi to accelerate Gysela application

Author

Matthieu Haefele, T. Cartier-Michaud, Guillaume Latu, Julien Bigot, Virginie Grandgirard, Fabien Rozar, Institut de Recherche sur la Fusion par confinement Magnétique (IRFM), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Max-Planck-Institut für Plasmaphysik [Garching] (IPP), Maison de la Simulation (MDLS), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut National de Recherche en Informatique et en Automatique (Inria)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Martin Campos Pinto and Frédérique Charles, EDP Sciences, ANR-10-BLAN-0941,GYPSI,Simulation GYrocinétique haute Performance Pour ITER(2010), and ANR-10-G8EX-0005,Nu-FuSe(2010)

Subjects
FOS: Computer and information sciences, Intel xeon phi coprocessor, FOS: Physical sciences, 010103 numerical & computational mathematics, 02 engineering and technology, Parallel computing, 01 natural sciences, Porting, Execution time, 0202 electrical engineering, electronic engineering, information engineering, Code (cryptography), QA1-939, 0101 mathematics, 020203 distributed computing, T57-57.97, Computer Science - Performance, Applied mathematics. Quantitative methods, Computational Physics (physics.comp-ph), Performance (cs.PF), [INFO.INFO-PF]Computer Science [cs]/Performance [cs.PF], Computer Science - Distributed, Parallel, and Cluster Computing, Vectorization (mathematics), Distributed, Parallel, and Cluster Computing (cs.DC), Physics - Computational Physics, Xeon Phi, Mathematics, Interpolation
Abstract

This work describes the challenges presented by porting parts ofthe Gysela code to the Intel Xeon Phi coprocessor, as well as techniques used for optimization, vectorization and tuning that can be applied to other applications. We evaluate the performance of somegeneric micro-benchmark on Phi versus Intel Sandy Bridge. Several interpolation kernels useful for the Gysela application are analyzed and the performance are shown. Some memory-bound and compute-bound kernels are accelerated by a factor 2 on the Phi device compared to Sandy architecture. Nevertheless, it is hard, if not impossible, to reach a large fraction of the peek performance on the Phi device,especially for real-life applications as Gysela. A collateral benefit of this optimization and tuning work is that the execution time of Gysela (using 4D advections) has decreased on a standard architecture such as Intel Sandy Bridge., Comment: submitted to ESAIM proceedings for CEMRACS 2014 summer school version reviewed

Published
2016
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3. Scaling gysela code beyond 32K-cores on bluegene/Q***

Author

Virginie Grandgirard, Guillaume Latu, Olivier Thomine, Julien Bigot, C. Passeron, and Fabien Rozar

Subjects
T57-57.97, Applied mathematics. Quantitative methods, Computer science, Parallel computing, Blue gene, symbols.namesake, Factor (programming language), symbols, Code (cryptography), QA1-939, Scaling, computer, Lagrangian, Simulation, Mathematics, computer.programming_language
Abstract

Gyrokinetic simulations lead to huge computational needs. Up to now, the semi- Lagrangian code Gysela performed large simulations using a few thousands cores (8k cores typically). Simulation with finer resolutions and with kinetic electrons are expected to increase those needs by a huge factor, providing a good example of applications requiring Exascale machines. This paper presents our work to improve Gysela in order to target an architecture that presents one possible way towards Exascale: the Blue Gene/Q. After analyzing the limitations of the code on this architecture, we have implemented three kinds of improvement: computational performance improvements, memory consumption improvements and disk i/o improvements. As a result, we show that the code now scales beyond 32k cores with much improved performances. This will make it possible to target the most powerful machines available and thus handle much larger physical cases.

Published
2013

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