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84 results on '"Bramas, Bérenger"'

1. Exploiting ray tracing technology through OptiX to compute particle interactions with cutoff in a 3D environment on GPU

Author: David, Algis and Bramas, Bérenger
Subjects: Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Graphics
Abstract: Computing on graphics processing units (GPUs) has become standard in scientific computing, allowing for incredible performance gains over classical CPUs for many computational methods. As GPUs were originally designed for 3D rendering, they still have several features for that purpose that are not used in scientific computing. Among them, ray tracing is a powerful technology used to render 3D scenes. In this paper, we propose exploiting ray tracing technology to compute particle interactions with a cutoff distance in a 3D environment. We describe algorithmic tricks and geometric patterns to find the interaction lists for each particle. This approach allows us to compute interactions with quasi-linear complexity in the number of particles without building a grid of cells or an explicit kd-tree. We compare the performance of our approach with a classical approach based on a grid of cells and show that, currently, ours is slower in most cases but could pave the way for future methods., Comment: https://gitlab.inria.fr/bramas/particle-interaction-with-optix more...
Published: 2024

2. Efficient GPU Implementation of Particle Interactions with Cutoff Radius and Few Particles per Cell

Author: Algis, David, Bramas, Berenger, Darles, Emmanuelle, and Aveneau, Lilian
Subjects: Computer Science - Distributed, Parallel, and Cluster Computing
Abstract: This paper presents novel approaches to parallelizing particle interactions on a GPU when there are few particles per cell and the interactions are limited by a cutoff distance. The paper surveys classical algorithms and then introduces two alternatives that aim to utilize shared memory. The first approach copies the particles of a sub-box, while the second approach loads particles in a pencil along the X-axis. The different implementations are compared on three GPU models using Cuda and Hip. The results show that the X-pencil approach can provide a significant speedup but only in very specific cases., Comment: https://gitlab.inria.fr/bramas/gpu-particle-interaction more...
Published: 2024

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3. Specx: a C++ task-based runtime system for heterogeneous distributed architectures

Author: Cardosi, Paul and Bramas, Bérenger
Subjects: Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Software Engineering
Abstract: Parallelization is needed everywhere, from laptops and mobile phones to supercomputers. Among parallel programming models, task-based programming has demonstrated a powerful potential and is widely used in high-performance scientific computing. Not only does it allow for efficient parallelization across distributed heterogeneous computing nodes, but it also allows for elegant source code structuring by describing hardware-independent algorithms. In this paper, we present Specx, a task-based runtime system written in modern C++. Specx supports distributed heterogeneous computing by simultaneously exploiting CPUs and GPUs (CUDA/HIP) and incorporating communication into the task graph. We describe the specificities of Specx and demonstrate its potential by running parallel applications., Comment: Research report. https://gitlab.inria.fr/bramas/specx more...
Published: 2023

4. SPC5: an efficient SpMV framework vectorized using ARM SVE and x86 AVX-512

Author: Regnault, Evann and Bramas, Berenger
Subjects: Computer Science - Distributed, Parallel, and Cluster Computing
Abstract: The sparse matrix/vector product (SpMV) is a fundamental operation in scientific computing. Having access to an efficient SpMV implementation is therefore critical, if not mandatory, to solve challenging numerical problems. The ARM-based AFX64 CPU is a modern hardware component that equips one of the fastest supercomputers in the world. This CPU supports the Scalable Vector Extension (SVE) vectorization technology, which has been less investigated than the classic x86 instruction set architectures. In this paper, we describe how we ported the SPC5 SpMV framework on AFX64 by converting AVX512 kernels to SVE. In addition, we present performance results by comparing our kernels against a standard CSR kernel for both Intel-AVX512 and Fujitsu-ARM-SVE architectures., Comment: https://gitlab.inria.fr/bramas/spc5 more...
Published: 2023

5. Autovesk: Automatic vectorized code generation from unstructured static kernels using graph transformations

Author: Tayeb, Hayfa, Paillat, Ludovic, and Bramas, Berenger
Subjects: Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Programming Languages
Abstract: Leveraging the SIMD capability of modern CPU architectures is mandatory to take full benefit of their increasing performance. To exploit this feature, binary executables must be explicitly vectorized by the developers or an automatic vectorization tool. This why the compilation research community has created several strategies to transform a scalar code into a vectorized implementation. However, the majority of the approaches focus on regular algorithms, such as affine loops, that can be vectorized with few data transformations. In this paper, we present a new approach that allow automatically vectorizing scalar codes with chaotic data accesses as long as their operations can be statically inferred. We describe how our method transforms a graph of scalar instructions into a vectorized one using different heuristics with the aim of reducing the number or cost of the instructions. Finally, we demonstrate the interest of our approach on various computational kernels using Intel AVX-512 and ARM SVE., Comment: Preprint more...
Published: 2023

6. An Efficient Particle Tracking Algorithm for Large-Scale Parallel Pseudo-Spectral Simulations of Turbulence

Author: Lalescu, Cristian C., Bramas, Bérenger, Rampp, Markus, and Wilczek, Michael
Subjects: Physics - Fluid Dynamics, Computer Science - Distributed, Parallel, and Cluster Computing, Physics - Computational Physics
Abstract: Particle tracking in large-scale numerical simulations of turbulent flows presents one of the major bottlenecks in parallel performance and scaling efficiency. Here, we describe a particle tracking algorithm for large-scale parallel pseudo-spectral simulations of turbulence which scales well up to billions of tracer particles on modern high-performance computing architectures. We summarize the standard parallel methods used to solve the fluid equations in our hybrid MPI/OpenMP implementation. As the main focus, we describe the implementation of the particle tracking algorithm and document its computational performance. To address the extensive inter-process communication required by particle tracking, we introduce a task-based approach to overlap point-to-point communications with computations, thereby enabling improved resource utilization. We characterize the computational cost as a function of the number of particles tracked and compare it with the flow field computation, showing that the cost of particle tracking is very small for typical applications. more...
Published: 2021
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7. Automatic task-based parallelization of C++ applications by source-to-source transformations

Author: Kusoglu, Garip, Bramas, Berenger, and Genaud, Stephane
Subjects: Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Programming Languages
Abstract: Currently, multi/many-core CPUs are considered standard in most types of computers including, mobile phones, PCs or supercomputers. However, the parallelization of applications as well as refactoring/design of applications for efficient hardware usage remains restricted to experts who have advanced technical knowledge and who can invest time tuning their software. In this context, the compilation community has proposed different methods for automatic parallelization, but their focus is traditionally on loops and nested loops with the support of polyhedral techniques. In this study, we propose a new approach to transform sequential C++ source code into a task-based parallel one by inserting annotations. We explain the different mechanisms we used to create tasks at each function/method call, and how we can limit the number of tasks. Our method can be implemented on top of the OpenMP 4.0 standard. It is compiler-independent and can rely on external well-optimized OpenMP libraries. Finally, we provide preliminary performance results that illustrate the potential of our method., Comment: Published at Compas 2020 more...
Published: 2021

8. A fast vectorized sorting implementation based on the ARM scalable vector extension (SVE)

Author: Bramas, Bérenger
Subjects: Computer Science - Distributed, Parallel, and Cluster Computing
Abstract: The way developers implement their algorithms and how these implementations behave on modern CPUs are governed by the design and organization of these. The vectorization units (SIMD) are among the few CPUs' parts that can and must be explicitly controlled. In the HPC community, the x86 CPUs and their vectorization instruction sets were de-facto the standard for decades. Each new release of an instruction set was usually a doubling of the vector length coupled with new operations. Each generation was pushing for adapting and improving previous implementations. The release of the ARM scalable vector extension (SVE) changed things radically for several reasons. First, we expect ARM processors to equip many supercomputers in the next years. Second, SVE's interface is different in several aspects from the x86 extensions as it provides different instructions, uses a predicate to control most operations, and has a vector size that is only known at execution time. Therefore, using SVE opens new challenges on how to adapt algorithms including the ones that are already well-optimized on x86. In this paper, we port a hybrid sort based on the well-known Quicksort and Bitonic-sort algorithms. We use a Bitonic sort to process small partitions/arrays and a vectorized partitioning implementation to divide the partitions. We explain how we use the predicates and how we manage the non-static vector size. We explain how we efficiently implement the sorting kernels. Our approach only needs an array of O(log N) for the recursive calls in the partitioning phase, both in the sequential and in the parallel case. We test the performance of our approach on a modern ARMv8.2 and assess the different layers of our implementation by sorting/partitioning integers, double floating-point numbers, and key/value pairs of integers. Our approach is faster than the GNU C++ sort algorithm by a speedup factor of 4 on average., Comment: https://gitlab.inria.fr/bramas/arm-sve-sort https://peerj.com/articles/cs-769/ more...
Published: 2021
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9. On the improvement of the in-place merge algorithm parallelization

Author: Bramas, Berenger and Bramas, Quentin
Subjects: Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Data Structures and Algorithms
Abstract: In this paper, we present several improvements in the parallelization of the in-place merge algorithm, which merges two contiguous sorted arrays into one with an O(T) space complexity (where T is the number of threads). The approach divides the two arrays into as many pairs of partitions as there are threads available; such that each thread can later merge a pair of partitions independently of the others. We extend the existing method by proposing a new algorithm to find the median of two partitions. Additionally, we provide a new strategy to divide the input arrays where we minimize the data movement, but at the cost of making this stage sequential. Finally, we provide the so-called linear shifting algorithm that swaps two partitions in-place with contiguous data access. We emphasize that our approach is straightforward to implement and that it can also be used for external (out of place) merging. The results demonstrate that it provides a significant speedup compared to sequential executions, when the size of the arrays is greater than a thousand elements. more...
Published: 2020

10. GPU Code Generation of Cardiac Electrophysiology Simulation with MLIR

Author: Trevisan Jost, Tiago, Thangamani, Arun, Colin, Raphaël, Loechner, Vincent, Genaud, Stéphane, Bramas, Bérenger, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Cano, José, editor, Dikaiakos, Marios D., editor, Papadopoulos, George A., editor, Pericàs, Miquel, editor, and Sakellariou, Rizos, editor more...
Published: 2023
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11. An Integral Equation Formulation of the $N$-body Dielectric Spheres Problem. Part II: Complexity Analysis

Author: Bramas, Bérenger, Hassan, Muhammad, and Stamm, Benjamin
Subjects: Mathematics - Numerical Analysis, 65F35, 65N12, 65N15, 65N35, 65R20
Abstract: This article is the second in a series of two papers concerning the mathematical study of a boundary integral equation of the second kind that describes the interaction of $N$ dielectric spherical particles undergoing mutual polarisation. The first article presented the numerical analysis of the Galerkin method used to solve this boundary integral equation and derived $N$-independent convergence rates for the induced surface charges and total electrostatic energy. The current article will focus on computational aspects of the algorithm. We provide a convergence analysis of the iterative method used to solve the underlying linear system and show that the number of liner solver iterations required to obtain a solution is independent of $N$. Additionally, we present two linear scaling solution strategies for the computation of the approximate induced surface charges. Finally, we consider a series of numerical experiments designed to validate our theoretical results and explore the dependence of the numerical errors and computational cost of solving the underlying linear system on different system parameters., Comment: 29 Page article more...
Published: 2019

12. GPU Code Generation of Cardiac Electrophysiology Simulation with MLIR

Author: Trevisan Jost, Tiago, primary, Thangamani, Arun, additional, Colin, Raphaël, additional, Loechner, Vincent, additional, Genaud, Stéphane, additional, and Bramas, Bérenger, additional
Published: 2023
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13. Towards EXtreme scale technologies and accelerators for euROhpc hw/Sw supercomputing applications for exascale: The TEXTAROSSA approach

Author: Agosta, Giovanni, Aldinucci, Marco, Alvarez, Carlos, Ammendola, Roberto, Arfat, Yasir, Beaumont, Olivier, Bernaschi, Massimo, Biagioni, Andrea, Boccali, Tommaso, Bramas, Berenger, Brandolese, Carlo, Cantalupo, Barbara, Carrozzo, Mauro, Cattaneo, Daniele, Celestini, Alessandro, Celino, Massimo, Colonnelli, Iacopo, Cretaro, Paolo, D’Ambra, Pasqua, Danelutto, Marco, Esposito, Roberto, Eyraud-Dubois, Lionel, Filgueras, Antonio, Fornaciari, William, Frezza, Ottorino, Galimberti, Andrea, Giacomini, Francesco, Goglin, Brice, Gregori, Daniele, Guermouche, Abdou, Iannone, Francesco, Kulczewski, Michal, Lo Cicero, Francesca, Lonardo, Alessandro, Martinelli, Alberto R., Martinelli, Michele, Martorell, Xavier, Massari, Giuseppe, Montangero, Simone, Mittone, Gianluca, Namyst, Raymond, Oleksiak, Ariel, Palazzari, Paolo, Paolucci, Pier Stanislao, Reghenzani, Federico, Rossi, Cristian, Saponara, Sergio, Simula, Francesco, Terraneo, Federico, Thibault, Samuel, Torquati, Massimo, Turisini, Matteo, Vicini, Piero, Vidal, Miquel, Zoni, Davide, and Zummo, Giuseppe more...
Published: 2022
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14. Increasing the Degree of Parallelism Using Speculative Execution in Task-based Runtime Systems

Author: Bramas, Bérenger
Subjects: Computer Science - Distributed, Parallel, and Cluster Computing
Abstract: Task-based programming models have demonstrated their efficiency in the development of scientific applications on modern high-performance platforms. They allow delegation of the management of parallelization to the runtime system (RS), which is in charge of the data coherency, the scheduling, and the assignment of the work to the computational units. However, some applications have a limited degree of parallelism such that no matter how efficient the RS implementation, they may not scale on modern multicore CPUs. In this paper, we propose using speculation to unleash the parallelism when it is uncertain if some tasks will modify data, and we formalize a new methodology to enable speculative execution in a graph of tasks. This description is partially implemented in our new C++ RS called SPETABARU, which is capable of executing tasks in advance if some others are not certain to modify the data. We study the behavior of our approach to compute Monte Carlo and replica exchange Monte Carlo simulations., Comment: 24 pages, https://peerj.com/articles/cs-183/ more...
Published: 2018
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15. Computing the sparse matrix vector product using block-based kernels without zero padding on processors with AVX-512 instructions

Author: Bramas, Berenger and Kus, Pavel
Subjects: Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Mathematical Software, Computer Science - Performance
Abstract: The sparse matrix-vector product (SpMV) is a fundamental operation in many scientific applications from various fields. The High Performance Computing (HPC) community has therefore continuously invested a lot of effort to provide an efficient SpMV kernel on modern CPU architectures. Although it has been shown that block-based kernels help to achieve high performance, they are difficult to use in practice because of the zero padding they require. In the current paper, we propose new kernels using the AVX-512 instruction set, which makes it possible to use a blocking scheme without any zero padding in the matrix memory storage. We describe mask-based sparse matrix formats and their corresponding SpMV kernels highly optimized in assembly language. Considering that the optimal blocking size depends on the matrix, we also provide a method to predict the best kernel to be used utilizing a simple interpolation of results from previous executions. We compare the performance of our approach to that of the Intel MKL CSR kernel and the CSR5 open-source package on a set of standard benchmark matrices. We show that we can achieve significant improvements in many cases, both for sequential and for parallel executions. Finally, we provide the corresponding code in an open source library, called SPC5., Comment: Published in Peer J CS more...
Published: 2018

16. A Novel Hybrid Quicksort Algorithm Vectorized using AVX-512 on Intel Skylake

Author: Bramas, Berenger
Subjects: Computer Science - Mathematical Software
Abstract: The modern CPU's design, which is composed of hierarchical memory and SIMD/vectorization capability, governs the potential for algorithms to be transformed into efficient implementations. The release of the AVX-512 changed things radically, and motivated us to search for an efficient sorting algorithm that can take advantage of it. In this paper, we describe the best strategy we have found, which is a novel two parts hybrid sort, based on the well-known Quicksort algorithm. The central partitioning operation is performed by a new algorithm, and small partitions/arrays are sorted using a branch-free Bitonic-based sort. This study is also an illustration of how classical algorithms can be adapted and enhanced by the AVX-512 extension. We evaluate the performance of our approach on a modern Intel Xeon Skylake and assess the different layers of our implementation by sorting/partitioning integers, double floating-point numbers, and key/value pairs of integers. Our results demonstrate that our approach is faster than two libraries of reference: the GNU \emph{C++} sort algorithm by a speedup factor of 4, and the Intel IPP library by a speedup factor of 1.4., Comment: 8 pages, research paper more...
Published: 2017
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17. Optimized M2L Kernels for the Chebyshev Interpolation based Fast Multipole Method

Author: Messner, Matthias, Bramas, Bérenger, Coulaud, Olivier, and Darve, Eric
Subjects: Computer Science - Numerical Analysis, Computer Science - Computational Engineering, Finance, and Science, Computer Science - Mathematical Software, Mathematics - Numerical Analysis
Abstract: A fast multipole method (FMM) for asymptotically smooth kernel functions (1/r, 1/r^4, Gauss and Stokes kernels, radial basis functions, etc.) based on a Chebyshev interpolation scheme has been introduced in [Fong et al., 2009]. The method has been extended to oscillatory kernels (e.g., Helmholtz kernel) in [Messner et al., 2012]. Beside its generality this FMM turns out to be favorable due to its easy implementation and its high performance based on intensive use of highly optimized BLAS libraries. However, one of its bottlenecks is the precomputation of the multiple-to-local (M2L) operator, and its higher number of floating point operations (flops) compared to other FMM formulations. Here, we present several optimizations for that operator, which is known to be the costliest FMM operator. The most efficient ones do not only reduce the precomputation time by a factor up to 340 but they also speed up the matrix-vector product. We conclude with comparisons and numerical validations of all presented optimizations. more...
Published: 2012

18. SPC5: An efficient SpMV framework vectorized using ARM SVE and x86 AVX-512

Author: Regnault, Evann, primary and Bramas, Bérenger, additional
Published: 2024
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19. Autovesk: Automatic Vectorized Code Generation from Unstructured Static Kernels Using Graph Transformations

Author: Tayeb, Hayfa, primary, Paillat, Ludovic, additional, and Bramas, Bérenger, additional
Published: 2023
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20. Extending the Task Dataflow Model with Speculative Data Accesses

Author: Souris, Anastasios, Bramas, Bérenger, Clauss, Philippe, Compilation pour les Architectures MUlti-coeurS (CAMUS), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)-École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), Université de Strasbourg (UNISTRA), and ANR-21-CE25-0009,AUTOSPEC,Parallélisation Automatique à Base de Tâches avec Exécution Spéculative(2021) more...
Subjects: [INFO]Computer Science [cs], [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC]
Abstract: International audience; In this paper, we describe a data-centric version-based approach to extending the task dataflow model with new access types that provide speculation capabilities to applications. We further describe the performance benefits of such a model using two scenarios that model realistic applications ; the parallelization of a finite-state machine and Monte-Carlo simulations. more...
Published: 2023

21. Autovesk: Automatic Vectorized Code Generation from Unstructured Static Kernels Using Graph Transformations.

Author: TAYEB, HAYFA, PAILLAT, LUDOVIC, and BRAMAS, BÉRENGER
Published: 2024
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22. Time-domain BEM for the wave equation on distributed-heterogeneous architectures: A blocking approach

Author: Bramas, Berenger, Coulaud, Olivier, and Sylvand, Guillaume
Published: 2015
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23. Time-Domain BEM for the Wave Equation: Optimization and Hybrid Parallelization

Author: Bramas, Berenger, Coulaud, Olivier, Sylvand, Guillaume, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Silva, Fernando, editor, Dutra, Inês, editor, and Santos Costa, Vítor, editor more...
Published: 2014
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24. MulTreePrio: Scheduling task-based applications for heterogeneous computing systems

Author: Tayeb, Hayfa, Bramas, Bérenger, Guermouche, Abdou, Faverge, Mathieu, Compilation pour les Architectures MUlti-coeurS (CAMUS), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)-École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Sciences de l'Image, de l'Informatique et de la Télédétection, équipe ICPS (LSIIT / ICPS), Centre National de la Recherche Scientifique (CNRS), Université Louis Pasteur - Strasbourg I-Centre National de la Recherche Scientifique (CNRS), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), High-End Parallel Algorithms for Challenging Numerical Simulations (HiePACS), 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, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), European Project: 956831,TEXTAROSSA(2021), Bramas, Bérenger, and Towards EXtreme scale Technologies and Accelerators for euROhpc hw/Sw Supercomputing Applications for exascale - TEXTAROSSA - 2021-01-01 - 2024-01-01 - 956831 - VALID more...
Subjects: Scheduling, DAG, [INFO.INFO-DC] Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], Task-based applications, Heterogeneous systems
Abstract: National audience; Effective scheduling is crucial for task-based applications to achieve high performance in heterogeneous computing systems. These applications are usually represented by directed acyclic graphs (DAG). In this paper, we present a dynamic scheduling technique for DAGs intending to minimize the overall completion time of the parallelized applications. We introduce MulTreePrio, a novel scheduler based on a set of balanced trees data structure. The assignment of tasks to available resources is done according to priority scores per task for each type of processing unit. These scores are computed through heuristics built according to a set of rules that our scheduler should fulfil. We simulate the scheduling on three DAGs coming from numerical kernels with different configurations and we compare its behavior with both dynamic schedulers and static scheduling techniques based on the critical path. We show the efficiency of our scheduler with an average speedup of x2 with respect to the dynamic scheduler and x0,99 compared to the critical path-based scheduler. MulTreePrio is promising and in future works, it will be integrated into a task-based runtime system and tested in real-life scenarios. more...
Published: 2022

25. Lifting Code Generation of Cardiac Physiology Simulation to Novel Compiler Technology

Author: Thangamani, Arun, primary, Jost, Tiago Trevisan, additional, Loechner, Vincent, additional, Genaud, Stéphane, additional, and Bramas, Bérenger, additional
Published: 2023
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26. Artifact for Lifting Code Generation of Cardiac Physiology Simulation to Novel Compiler Technology

Author: Thangamani, Arun, primary, Jost, Tiago Trevisan, additional, Loechner, Vincent, additional, Genaud, Stéphane, additional, and Bramas, Bérenger, additional
Published: 2023
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27. Parallel kinetic scheme in complex toroidal geometry

Author: Boileau, Matthieu, Bramas, Bérenger, Franck, Emmanuel, Hélie, Romane, Helluy, Philippe, Navoret, Laurent, 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, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Compilation pour les Architectures MUlti-coeurS (CAMUS), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)-École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), and Helluy, Philippe more...
Subjects: 35L65, 65M12, 35Q83, 82D10, Physics::Plasma Physics, plasma physics, [MATH.MATH-AP]Mathematics [math]/Analysis of PDEs [math.AP], [MATH.MATH-AP] Mathematics [math]/Analysis of PDEs [math.AP], kinetic scheme, discontinuous Galerkin
Abstract: We present an efficient solver for the conservative transport equation with variable coefficients in complex toroidal geometries. The solver is based on a kinetic formulation resembling the Lattice-Boltzmann approach.The chosen formalism allows to obtain an explicit and conservative scheme that requires no matrix inversion and whose CFL stability condition is independent from the poloidal dynamics. We present the method and its optimized parallel implementation on toroidal geometries. Two and three dimensional plasma physics test cases are carried out. more...
Published: 2022
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28. Parallel kinetic scheme for transport equations in complex toroidal geometry

Author: Boileau, Matthieu, primary, Bramas, Bérenger, additional, Franck, Emmanuel, additional, Hélie, Romane, additional, Helluy, Philippe, additional, and Navoret, Laurent, additional
Published: 2022
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29. Automated prioritizing heuristics for parallel task graph scheduling in heterogeneous computing

Author: Flint, Clément, primary, Paillat, Ludovic, additional, and Bramas, Bérenger, additional
Published: 2022
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30. An efficient particle tracking algorithm for large-scale parallel pseudo-spectral simulations of turbulence

Author: Lalescu, Cristian C., primary, Bramas, Bérenger, additional, Rampp, Markus, additional, and Wilczek, Michael, additional
Published: 2022
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31. Automatic task-based parallelization of C++ applications by source-to-source transformations

Author: Kusoglu, Garip, Bramas, Bérenger, Genaud, Stéphane, Compilation pour les Architectures MUlti-coeurS (CAMUS), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et nanosciences d'Alsace (FMNGE), Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA), Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)-École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), and Bramas, Bérenger more...
Subjects: FOS: Computer and information sciences, Computer Science - Programming Languages, Computer Science - Distributed, Parallel, and Cluster Computing, Task-based, Object-oriented, [INFO.INFO-DC] Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], Parallelization, Distributed, Parallel, and Cluster Computing (cs.DC), [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], Compilation, Programming Languages (cs.PL)
Abstract: Currently, multi/many-core CPUs are considered standard in most types of computers including, mobile phones, PCs or supercomputers. However, the parallelization of applications as well as refactoring/design of applications for efficient hardware usage remains restricted to experts who have advanced technical knowledge and who can invest time tuning their software. In this context, the compilation community has proposed different methods for automatic parallelization, but their focus is traditionally on loops and nested loops with the support of polyhedral techniques. In this study, we propose a new approach to transform sequential C++ source code into a task-based parallel one by inserting annotations. We explain the different mechanisms we used to create tasks at each function/method call, and how we can limit the number of tasks. Our method can be implemented on top of the OpenMP 4.0 standard. It is compiler-independent and can rely on external well-optimized OpenMP libraries. Finally, we provide preliminary performance results that illustrate the potential of our method., Comment: Published at Compas 2020 more...
Published: 2020

32. A fast vectorized sorting implementation based on the ARM scalable vector extension (SVE)

Author: Bramas, Bérenger, primary
Published: 2021
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33. Shape- and scale-dependent coupling between spheroids and velocity gradients in turbulence

Author: Pujara, Nimish, primary, Arguedas-Leiva, José-Agustín, additional, Lalescu, Cristian C., additional, Bramas, Bérenger, additional, and Wilczek, Michael, additional
Published: 2021
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34. Finding new heuristics for automated task prioritizing in heterogeneous computing

Author: Flint, Clément, Bramas, Bérenger, Compilation pour les Architectures MUlti-coeurS (CAMUS), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et nanosciences d'Alsace (FMNGE), Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA), Université de Strasbourg (UNISTRA), ICPS, Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)-École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), and Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS) more...
Subjects: [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC]
Abstract: In many sciences, processing costly computations has become frequent and the execution time of an application is often viewed as a bottleneck. High-Performance Computing is a growing area of interest whose role is to reduce the execution time of these applications by optimizing their granularity, data accesses, data transfers, etc. During the late 2010 decade, Graphics Processing Units have started to become a popular solution for speeding up programs. For some operations, using GPUs instead of CPUs drastically speeds up the computation time thanks to their massive parallelization potential. There are, however, downsides to using GPUs along with CPUs. Firstly, it adds costly data transfers to make data available to the GPUs. Secondly, scheduling decisions are more complex and may, therefore, be less efficient. In this study, we improve Heteroprio, a hybrid CPU/GPU scheduler that works with user-defined CPU and GPU priorities. Our improved version now affects priorities automatically, based on heuristics that have been validated on theoretical executions. more...
Published: 2020

35. An integral equation formulation of the N-body dielectric spheres problem. Part II: complexity analysis

Author: Bramas, Bérenger, primary, Hassan, Muhammad, additional, and Stamm, Benjamin, additional
Published: 2021
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36. Optimization of a discontinuous Galerkin solver with OpenCL and StarPU

Author: Bramas, Bérenger, Helluy, Philippe, Mendoza, Laura, Weber, Bruno, Compilation pour les Architectures MUlti-coeurS (CAMUS), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)-École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Réseau nanophotonique et optique, Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Matériaux et nanosciences d'Alsace (FMNGE), Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), TOkamaks and NUmerical Simulations (TONUS), Institut de Recherche Mathématique Avancée (IRMA), 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, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), AxesSim, This work has obtained support from the ANR EXAMAG project and the BPI France HOROCH project., ANR-15-SPPE-0002,EXAMAG,EXAMAG - Exascale simulations of the magnetic universe(2015), École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)-École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), and Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS) more...
Subjects: OpenCL, Discontinuous Galerkin, [MATH.MATH-AP]Mathematics [math]/Analysis of PDEs [math.AP], StarPU, hybrid parallelism DG solver with StarPU
Abstract: International audience; Since the recent advance in microprocessor design, the optimization of computing software becomes more and more technical. One of the difficulties is to transform sequential algorithms into parallel ones. A possible solution is the task-based design. In this approach, it is possible to describe the parallelization possibilities of the algorithm automatically. The task-based design is also a good strategy to optimize software in an incremental way. The objective of this paper is to describe a practical experience of a task-based parallelization of a Discontinuous Galerkin method in the context of electromagnetic simulations. The task-based description is managed by the StarPU runtime. Additional acceleration is obtained by OpenCL. more...
Published: 2020

37. Improving parallel executions by increasing task granularity in task-based runtime systems using acyclic DAG clustering

Author: Bramas, Bérenger, Ketterlin, Alain, Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Compilation pour les Architectures MUlti-coeurS (CAMUS), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)-École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), The experiments presented in this paper were carried out using the PlaFRIM experimental testbed, supported by Inria, CNRS (LABRI and IMB), Université de Bordeaux, Bordeaux INP and Conseil Régional d’Aquitaine (see https://www.plafrim.fr/)., Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et nanosciences d'Alsace (FMNGE), Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA), and Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg) more...
Subjects: Scientific Computing and Simulation, Algorithms and Analysis of Algorithms, Distributed and Parallel Computing, Task-based, DAG, lcsh:Electronic computers. Computer science, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], Graph, Clustering, Partitioning, lcsh:QA75.5-76.95, Scientific Computing and Simulation Keywords Task-based
Abstract: International audience; The task-based approach is a parallelization paradigm in which an algorithm is transformed into a direct acyclic graph of tasks: the vertices are computational elements extracted from the original algorithm and the edges are dependencies between those. During the execution, the management of the dependencies adds an overhead that can become significant when the computational cost of the tasks is low. A possibility to reduce the makespan is to aggregate the tasks to make them heavier, while having fewer of them, with the objective of mitigating the importance of the overhead. In this paper, we study an existing clustering/partitioning strategy to speed up the parallel execution of a task-based application. We provide two additional heuristics to this algorithm and perform an in-depth study on a large graph set. In addition, we propose a new model to estimate the execution duration and use it to choose the proper granularity. We show that this strategy allows speeding up a real numerical application by a factor of 7 on a multi-core system. more...
Published: 2020
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38. Parallel lattice-boltzmann transport solver in complex geometry

Author: Boileau, Matthieu, Bramas, Bérenger, Franck, Emmanuel, Helluy, Philippe, Navoret, Laurent, 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, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Compilation pour les Architectures MUlti-coeurS (CAMUS), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et nanosciences d'Alsace (FMNGE), Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), and Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA) more...
Subjects: Physics::Plasma Physics, [MATH.MATH-AP]Mathematics [math]/Analysis of PDEs [math.AP]
Abstract: We present an efficient solver for the conservative transport equation with variable coefficients in complex geometries. The solver is based on a kinetic formulation resembling the Lattice-Boltzmann approach. The chosen formalism allows to obtaining an explicit and conservative scheme that requires no matrix inversion and that is unconditionally stable with respect to the time step size. We present the method and its optimized parallel implementation on complex toroidal geometries. This solver will be used as a building block for tokamak plasma physics simulation. more...
Published: 2019

39. Impact study of data locality on task-based applications through the Heteroprio scheduler

Author: Bramas, Bérenger, Compilation pour les Architectures MUlti-coeurS (CAMUS), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et nanosciences d'Alsace (FMNGE), Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA), plafrim, Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)-École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), and Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS) more...
Subjects: General Computer Science, Computer science, Distributed computing, Symmetric multiprocessor system, Starpu, 010103 numerical & computational mathematics, 02 engineering and technology, 01 natural sciences, lcsh:QA75.5-76.95, Scheduling (computing), Distributed and Parallel Computing, Task-based, 0202 electrical engineering, electronic engineering, information engineering, 0101 mathematics, 020203 distributed computing, Job shop scheduling, Stencil code, Scheduling, Locality, Load balancing (computing), HPC, lcsh:Electronic computers. Computer science, Data locality, Performance improvement, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], Heuristics
Abstract: International audience; The task-based approach has emerged as a viable way to effectively use modern heterogeneous computing nodes. It allows the development of parallel applications with an abstraction of the hardware by delegating task distribution and load balancing to a dynamic scheduler. In this organization, the scheduler is the most critical component that solves the DAG scheduling problem in order to select the right processing unit for the computation of each task. In this work, we extend our Heteroprio scheduler that was originally created to execute the fast multipole method on multi-GPUs nodes. We improve Heteroprio by taking into account data locality during task distribution. The main principle is to use different task-lists for the different memory nodes and to investigate how locality affinity between the tasks and the different memory nodes can be evaluated without looking at the tasks' dependencies. We evaluate the benefit of our method on two linear algebra applications and a stencil code. We show that simple heuristics can provide significant performance improvement and cut by more than half the total memory transfer of an execution. more...
Published: 2019
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40. SPETABARU: A Task-based Runtime System with Speculative Execution Capability

Author: Bramas, Bérenger, Compilation pour les Architectures MUlti-coeurS (CAMUS), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et nanosciences d'Alsace (FMNGE), Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA), CAMUS, Plafrim, Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)-École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), and Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS) more...
Subjects: Runtime, HPC, GPU, FMM, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC]
Abstract: International audience; While task-based programming models allow expressing the parallelism of algorithms finely, the traditional data accesses used in the sequential task-flow model (STF) can restrict the parallelism and hide useful information. In this presentation, we describe how more precise data accesses can be used to get better performance, and how uncertain modifications of the data by the tasks open the possibility for speculative execution. We detail different speculative execution models when this uncertainty exists. We also introduce our speculative runtime system, SPETABARU, and provide examples with the parallelization of the Monte Carlo and replica exchange Monte Carlo simulations. more...
Published: 2019

41. Impact study of data locality on task-based applications through the Heteroprio scheduler

Author: Bramas, Bérenger, primary
Published: 2019
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42. Increasing the degree of parallelism using speculative execution in task-based runtime systems

Author: Bramas, Bérenger, primary
Published: 2019
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43. Computing the sparse matrix vector product using block-based kernels without zero padding on processors with AVX-512 instructions

Author: Bramas, Bérenger, primary and Kus, Pavel, additional
Published: 2018
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44. Méthode des multipôles rapide à base de tâches pour des clusters de processeurs multicoeurs

Author: Agullo, Emmanuel, Bramas, Bérenger, Coulaud, Olivier, Khannouz, Martin, Stanisic, Luka, High-End Parallel Algorithms for Challenging Numerical Simulations (HiePACS), Laboratoire Bordelais de Recherche en Informatique (LaBRI), 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, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Max Planck Computing and Data Facility [Garching] (MPCDF), STatic Optimizations, Runtime Methods (STORM), Inria Bordeaux Sud-Ouest, plafrim, and 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 more...
Subjects: architecture multicœur, hybrid parallelization, méthode multipôles rapide, Calcul haute performance, moteur d’exécution, fast multipole method, OpenMP, high performance computing (HPC), runtime system, programmation à base de tâches, multicore processor, parallélisation hybride, task-based programming, FMM, MPI, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], cluster
Abstract: Most high-performance, scientific libraries have adopted hybrid parallelization schemes - such as the popular MPI+OpenMP hybridization - to benefit from the capacities of modern distributed-memory machines. While these approaches have shown to achieve high performance, they require a lot of effort to design and maintain sophisticated synchronization/communication strategies. On the other hand, task-based programming paradigms aim at delegating this burden to a runtime system for maximizing productivity. In this article, we assess the potential of task-based fast multipole methods (FMM) on clusters of multicore processors. We propose both a hybrid MPI+task FMM parallelization and a pure task-based parallelization where the MPI communications are implicitly handled by the runtime system. The latter approach yields a very compact code following a sequential task-based programming model. We show that task-based approaches can compete with a hybrid MPI+OpenMP highly optimized code and that furthermore the compact task-based scheme fully matches the performance of the sophisticated, hybrid MPI+task version, ensuring performance while maximizing productivity. We illustrate our discussion with the ScalFMM FMM library and the StarPU runtime system.; La plupart des bibliothèques scientifiques très performantes ont adopté des parallélisations hybrides - comme l’approche MPI+OpenMP - pour profiter des capacités des machines modernes à mémoire distribuée. Ces approches permettent d’obtenir de très hautes performances, mais elles nécessitent beaucoup d’efforts pour concevoir et pour maintenir des stratégies de synchronisation/communication sophistiquées. D’un autre côté, les paradigmes de programmation à base de tâches visent à déléguer ce fardeau à un moteur d'exécution pour maximiser la productivité. Dans cet article, nous évaluons le potentiel de la méthode des multipôles rapide (FMM) à base de tâches sur les clusters de processeurs multic\oe{}urs. Nous proposons deux types de parallélisation, une première approche hybride (MPI+Tâche) à base de tâches et d’appels à MPI pour gérer explicitement les communications et la deuxième uniquement à base de tâches où les communications MPI sont implicitement postées par le moteur d'exécution. Cette dernière approche conduit à un code très compact qui suit le modèle de programmation séquentiel à base de tâches. Nous montrons que cette approche rivalise avec le code hybride MPI+OpenMP fortement optimisé et qu'en outre le code compact atteint les performances de la version hybride MPI+Tâche, assurant une très haute performance tout en maximisant la productivité. Nous illustrons notre propos avec la bibliothèque FMM ScalFMM et le moteur d'exécution StarPU. more...
Published: 2017

45. Task-based fast multipole method for clusters of multicore processors

Author: Agullo, Emmanuel, Bramas, Bérenger, Coulaud, Olivier, Khannouz, Martin, Stanisic, Luka, High-End Parallel Algorithms for Challenging Numerical Simulations (HiePACS), 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)-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), Max Planck Computing and Data Facility [Garching] (MPCDF), STatic Optimizations, Runtime Methods (STORM), Inria Bordeaux Sud-Ouest, and plafrim more...
Subjects: architecture multicœur, hybrid parallelization, méthode multipôles rapide, Calcul haute performance, moteur d’exécution, fast multipole method, OpenMP, high performance computing (HPC), runtime system, programmation à base de tâches, multicore processor, parallélisation hybride, task-based programming, FMM, MPI, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], cluster
Abstract: Most high-performance, scientific libraries have adopted hybrid parallelization schemes - such as the popular MPI+OpenMP hybridization - to benefit from the capacities of modern distributed-memory machines. While these approaches have shown to achieve high performance, they require a lot of effort to design and maintain sophisticated synchronization/communication strategies. On the other hand, task-based programming paradigms aim at delegating this burden to a runtime system for maximizing productivity. In this article, we assess the potential of task-based fast multipole methods (FMM) on clusters of multicore processors. We propose both a hybrid MPI+task FMM parallelization and a pure task-based parallelization where the MPI communications are implicitly handled by the runtime system. The latter approach yields a very compact code following a sequential task-based programming model. We show that task-based approaches can compete with a hybrid MPI+OpenMP highly optimized code and that furthermore the compact task-based scheme fully matches the performance of the sophisticated, hybrid MPI+task version, ensuring performance while maximizing productivity. We illustrate our discussion with the ScalFMM FMM library and the StarPU runtime system.; La plupart des bibliothèques scientifiques très performantes ont adopté des parallélisations hybrides - comme l’approche MPI+OpenMP - pour profiter des capacités des machines modernes à mémoire distribuée. Ces approches permettent d’obtenir de très hautes performances, mais elles nécessitent beaucoup d’efforts pour concevoir et pour maintenir des stratégies de synchronisation/communication sophistiquées. D’un autre côté, les paradigmes de programmation à base de tâches visent à déléguer ce fardeau à un moteur d'exécution pour maximiser la productivité. Dans cet article, nous évaluons le potentiel de la méthode des multipôles rapide (FMM) à base de tâches sur les clusters de processeurs multic\oe{}urs. Nous proposons deux types de parallélisation, une première approche hybride (MPI+Tâche) à base de tâches et d’appels à MPI pour gérer explicitement les communications et la deuxième uniquement à base de tâches où les communications MPI sont implicitement postées par le moteur d'exécution. Cette dernière approche conduit à un code très compact qui suit le modèle de programmation séquentiel à base de tâches. Nous montrons que cette approche rivalise avec le code hybride MPI+OpenMP fortement optimisé et qu'en outre le code compact atteint les performances de la version hybride MPI+Tâche, assurant une très haute performance tout en maximisant la productivité. Nous illustrons notre propos avec la bibliothèque FMM ScalFMM et le moteur d'exécution StarPU. more...
Published: 2017

46. Modeling Irregular Kernels of Task-based codes: Illustration with the Fast Multipole Method

Author: Agullo, Emmanuel, Bramas, Bérenger, Coulaud, Olivier, Stanisic, Luka, Thibault, Samuel, STatic Optimizations, Runtime Methods (STORM), 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)-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), High-End Parallel Algorithms for Challenging Numerical Simulations (HiePACS), Max Planck Computing and Data Facility [Garching] (MPCDF), INRIA Bordeaux, PLAFRIM, and 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 more...
Subjects: Mathematical Software, Modeling and simulation, task-based programming, fast multipole method, Parallel computing methodologies, runtime system, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC]
Abstract: The significant increase of the hardware complexity that occurred in the last few years led the high performance community to design many scientific libraries according to a task-based parallelization. The modeling of the performance of the individual tasks (or kernels) they are composed of is crucial for facing multiple challenges as diverse as performing accurate performance predictions, designing robust scheduling algorithms, tuning the applications, etc. Fine-grain modeling such as emulation and cycle-accurate simulation may lead to very accurate results. However, not only their high cost may be prohibitive but they furthermore require a high fidelity modeling of the processor, which makes them hard to deploy in practice. In this paper, we propose an alternative coarse-grain, empirical methodology oblivious to both the target code and the hardware architecture, which leads to robust and accurate timing predictions. We illustrate our approach with a task-based Fast Multipole Method (FMM) algorithm, whose kernels are highly irregular, implemented in the \scalfmm library on top of the starpu task-based runtime system and the simgrid simulator.; L'augmentation significative de la complexité matérielle qui s'est produite ces quelques dernières années a amené la communauté de calcul haute performance à mettre au point de nombreuses bibliothèques scientifiques sur le principe d'une parallélisation à base de tâches. La modélisation de la performance des tâches individuelles (ou noyaux) qui les composent est cruciale pour faire face aux multiples challenges aussi variés que la réalisation de prédictions de performance précises, la mise au point d'algorithme d'ordonnancement robustes, l'optimisation des applications, etc. La modélisation à grain fin tel que l'émulation et la simulation à la précision du cycle peut permettre des résultats très précis. Toutefois, non seulement leur coût élevé peut être prohibitif mais elles requièrent de surcroît une modélisation très fidèle du processeur, ce qui les rend difficiles à déployer en pratique. Dans ce papier, nous proposons une méthodologie alternative, à plus gros grain, empirique, transparente à la fois pour le code et l'architecture cibles, ce qui permet des prédictions robustes et précises. Nous illustrons notre approche avec une méthode méthode multipolaire rapide (FMM) à base de tâches, dont les noyaux sont hautement irréguliers, implémentée dans la librairie ScalFMM au-dessus du moteur d'exécution StarPU et du simulateur SimGrid. more...
Published: 2017

47. New Computational Ordering to Reach High Performance in the Time-domain BEM for the Wave Equation

Author: Bramas, Bérenger, Coulaud, Olivier, Sylvand, Guillaume, High-End Parallel Algorithms for Challenging Numerical Simulations (HiePACS), 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)-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), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), EADS Innovation Works [Toulouse], EADS - European Aeronautic Defense and Space, Inria, Airbus, Conseil Regionale d'Aquitaine, Airbus, Inria, Plafrim, 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, Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), and Bramas, Bérenger more...
Subjects: [INFO.INFO-DC] Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC]
Abstract: International audience; The time-domain BEM problem for wave equation in acoustics and electromagnetism can be expressed as a sparse linear system composed of multiple interaction/convolution matrices. Solving this problem by using sparse matrix-vector products are inefficient to achieve high Flop-rate. In this presentation we introduce a novel approach based on reordering the interaction matrices. We describe this new algorithm which increases the Flop-rate and takes advantage of the SIMD instructions. We present how it changes the parallelization in both shared and distributed memory. Finally, we demonstrate the performance of our system by studying the sequential Flop-rate and the parallel scalability with an airplane test case and up to 32 nodes. more...
Published: 2014

48. Optimisation et parallèlisation de la méthode des élements frontières pour l’équation des ondes dans le domaine temporel

Author: Bramas, Bérenger, High-End Parallel Algorithms for Challenging Numerical Simulations (HiePACS), 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)-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), 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, Olivier Coulaud, and PLAFRIM more...
Subjects: Optimization, Programmation parallèle, Vectorisation, Parallel programming, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], Calcul haute performance, GPU, Acoustique, Electromagnetism, Méthode des éléments frontières, Vectorization, Électromagnétisme, Wave equation, Boundary element method, Équation des ondes, Optimisation, Acoustic, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], High-performance computing
Abstract: The time-domain BEM for the wave equation in acoustics and electromagnetism is used to simulatethe propagation of a wave with a discretization in time. It allows to obtain several frequencydomainresults with one solve. In this thesis, we investigate the implementation of an efficientTD-BEM solver using different approaches. We describe the context of our study and the TD-BEMformulation expressed as a sparse linear system composed of multiple interaction/convolutionmatrices. This system is naturally computed using the sparse matrix-vector product (SpMV). Wework on the limits of the SpMV kernel by looking at the matrix reordering and the behavior of ourSpMV kernels using vectorization (SIMD) on CPUs and an advanced blocking-layout on NvidiaGPUs. We show that this operator is not appropriate for our problem, and we then propose toreorder the original computation to get a special matrix structure. This new structure is called aslice matrix and is computed with a custom matrix/vector product operator. We present an optimizedimplementation of this operator on CPUs and Nvidia GPUs for which we describe advancedblocking schemes. The resulting solver is parallelized with a hybrid strategy above heterogeneousnodes and relies on a new heuristic to balance the work among the processing units. Due tothe quadratic complexity of this matrix approach, we study the use of the fast multipole method(FMM) for our time-domain BEM solver. We investigate the parallelization of the general FMMalgorithm using several paradigms in both shared and distributed memory, and we explain howmodern runtime systems are well-suited to express the FMM computation. Finally, we investigatethe implementation and the parametrization of an FMM kernel specific to our TD-BEM, and weprovide preliminary results.; La méthode des éléments frontières pour l’équation des ondes (BEM) est utilisée en acoustique eten électromagnétisme pour simuler la propagation d’une onde avec une discrétisation en temps(TD). Elle permet d’obtenir un résultat pour plusieurs fréquences à partir d’une seule résolution.Dans cette thèse, nous nous intéressons à l’implémentation efficace d’un simulateur TD-BEM sousdifférents angles. Nous décrivons le contexte de notre étude et la formulation utilisée qui s’exprimesous la forme d’un système linéaire composé de plusieurs matrices d’interactions/convolutions.Ce système est naturellement calculé en utilisant l’opérateur matrice/vecteur creux (SpMV). Nousavons travaillé sur la limite du SpMV en étudiant la permutation des matrices et le comportementde notre implémentation aidé par la vectorisation sur CPU et avec une approche par bloc surGPU. Nous montrons que cet opérateur n’est pas approprié pour notre problème et nous proposonsde changer l’ordre de calcul afin d’obtenir une matrice avec une structure particulière.Cette nouvelle structure est appelée une matrice tranche et se calcule à l’aide d’un opérateur spécifique.Nous décrivons des implémentations optimisées sur architectures modernes du calculhaute-performance. Le simulateur résultant est parallélisé avec une approche hybride (mémoirespartagées/distribuées) sur des noeuds hétérogènes, et se base sur une nouvelle heuristique pouréquilibrer le travail entre les processeurs. Cette approche matricielle a une complexité quadratiquesi bien que nous avons étudié son accélération par la méthode des multipoles rapides (FMM). Nousavons tout d’abord travaillé sur la parallélisation de l’algorithme de la FMM en utilisant différentsparadigmes et nous montrons comment les moteurs d’exécution sont adaptés pour relâcher le potentielde la FMM. Enfin, nous présentons des résultats préliminaires d’un simulateur TD-BEMaccéléré par FMM . more...
Published: 2016

49. ScalFMM: A Generic Parallel Fast Multipole Library

Author: Blanchard, Pierre, Bramas, Bérenger, Coulaud, Olivier, Darve, Eric, Dupuy, Laurent, Etcheverry, Arnaud, Sylvand, Guillaume, High-End Parallel Algorithms for Challenging Numerical Simulations (HiePACS), Laboratoire Bordelais de Recherche en Informatique (LaBRI), 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, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), Department of Mechanical Engineering [Stanford], Stanford University, Institute for Computational and Mathematical Engineering [Stanford] (ICME), Service des Recherches Métallurgiques Appliquées (SRMA), Département des Matériaux pour le Nucléaire (DMN), CEA-Direction des Energies (ex-Direction de l'Energie Nucléaire) (CEA-DES (ex-DEN)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-CEA-Direction des Energies (ex-Direction de l'Energie Nucléaire) (CEA-DES (ex-DEN)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Airbus Group Innovations [Suresnes], Airbus [France], HiePACS, FastLA, Optidis, Plafrim, 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, and Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB) more...
Subjects: ScalFMM, Time Domain, BEM, HPC, Dislocation Dynamics, FMM, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC]
Abstract: International audience; ScalFMM (Parallel Fast Multipole Library for Large Scale Simulations) offers all the functionalities needed to perform large parallel simulations while enabling an easy customization of the simulation components: kernels, particles and cells. We will present how we use our library on two kinds of application involving boundary integral representations of physical fields. The first one implements isotropic dislocation kernels for Dislocation Dynamics and the second a time dependent kernel for acoustic problems. more...
Published: 2015

50. Time-Domain BEM for the Wave Equation on Distributed-Heterogenous Architectures : a Blocking Approach

Author: Bramas, Bérenger, Coulaud, Olivier, Sylvand, Guillaume, High-End Parallel Algorithms for Challenging Numerical Simulations (HiePACS), 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)-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), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), EADS Innovation Works [Toulouse], EADS - European Aeronautic Defense and Space, Airbus Group, Inria and Conseil Regional d'Aquitaine, Inria Bordeaux Sud-Ouest, INRIA, Airbus, Inria, Plafrim, 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 Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS) more...
Subjects: BEM, Parallel Algorithms, GPU, CUDA, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], Multi-GPU, Time-domain, Hybrid parallelization
Abstract: The problem of time-domain BEM for the wave equation in acoustics and electromagnetism can be expressed as a sparse linear system composed of multiple interaction/convolution matrices. It can be solved using sparse matrix-vector products which are inefficient to achieve high Flop-rate whether on CPU or GPU. In this paper we extend the approach proposed in a previous work~\cite{bib:bramas} in which we re-order the computation to get a special matrices structure with one dense vector per row. This new structure is called a slice matrix and is computed with a custom matrix/vector product operator. In this study we present an optimized implementations of this operator on Nvidia GPU based on two blocking strategies. We explain how we can obtain multiple block-values from a slice and how these ones can be computed efficiently on GPU. We target heterogeneous nodes composed of CPU and GPU. In order to deal with the different efficiencies of the processing units we use a greedy heuristic that dynamically balances the work among the workers. We demonstrate the performance of our system by studying the quality of the balancing heuristic and the sequential Flop-rate of the blocked implementations. Finally, we validate our implementation with an industrial test case on 8 heterogeneous nodes each composed of 12 CPU and 3 GPU. more...
Published: 2014

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