Overland flow modelling with the Shallow Water Equation using a well balanced numerical scheme: Adding efficiency or just more complexity?

In the last decades, more or less complex physically-based hydrological models, have been developed that solve the shallow water equations or their approximations using various numerical methods. Model users may not necessarily know the different hypothesis lying behind these development and simplifications, and it might therefore be difficult to judge if a code is well adapted to their objectives and test case configurations. This paper aims at comparing the predictive abilities of different models and evaluating potential gain by using advanced numerical scheme for modelling runoff. We present four different codes, each one based on either shallow water or kinematic waves equations, and using either finite volume or finite difference method. We compare these four numerical codes on different test cases allowing to emphasize their main strengths and weaknesses. Results show that, for relatively simple configurations, kinematic waves equations solved with finite volume method represent an interesting option. Nevertheless, as it appears to be limited in case of discontinuous topography or strong spatial heterogeneities, for these cases we advise the use of shallow water equations solved with the finite volume method

Energy Efficient Seismic Wave Propagation Simulation on a Low-power Manycore Processor.

International audienceLarge-scale simulation of seismic wave propagation is an active research topic. Its high demand for processing power makes it a good match for High Performance Computing (HPC). Although we have observed a steady increase on the processing capabilities of HPC platforms, their energy efficiency is still lacking behind. In this paper, we analyze the use of a low-power manycore processor, the MPPA-256, for seismic wave propagation simulations. First we look at its peculiar characteristics such as limited amount of on-chip memory and describe the intricate solution we brought forth to deal with this processor's idiosyncrasies. Next, we compare the performance and energy efficiency of seismic wave propagation on MPPA-256 to other commonplace platforms such as general-purpose processors and a GPU. Finally, we wrap up with the conclusion that, even if MPPA-256 presents an increased software development complexity, it can indeed be used as an energy efficient alternative to current HPC platforms, resulting in up to 71% and 5.18x less energy than a GPU and a general-purpose processor, respectively

Relative performance projection on Arm architectures

International audienceWith the advent of multi-many-core processors and hardware accelerators, choosing a specific architecture to renew a supercomputer can become very tedious. This decision process should consider the current and future parallel application needs and the design of the target software stack. It should also consider the single-core behavior of the application as it is one of the performance limitations in today's machines. In such a scheme, performance hints on the impact of some hardware and software stack modifications are mandatory to drive this choice. This paper proposes a workflow for performance projection based on execution on an actual processor and the application's behavior. This projection evaluates the performance variation from an existing core of a processor to a hypothetical one to drive the design choice. For this purpose, we characterize the maximum sustainable performance of the target machine and analyze the application using the software stack of the target machine. To validate this approach, we apply it to three applications of the CORAL benchmark suite: LULESH, MiniFE, and Quicksilver, using a single-core of two Arm-based architectures: Marvell ThunderX2 and Arm Neoverse N1. Finally, we follow this validation work with an example of design-space exploration around the SVE vector size, the choice of DDR4 and HBM2, and the software stack choice on A64FX on our applications with a pool of three source architectures: Arm Neoverse N1, Marvell ThunderX2, and Fujitsu A64FX

Collaborative simulation and scientific big data analysis: Illustration for sustainability in natural hazards management and chemical process engineering

Classical approaches for remote visualization and collaboration used in Computer-Aided Design and Engineering (CAD/E) applications are no longer appropriate due to the increasing amount of data generated, especially using standard networks. We introduce a lightweight and computing platform for scientific simulation, collaboration in engineering, 3D visualization and big data management. This ICT based platform provides scientists an “easy-to-integrate” generic tool, thus enabling worldwide collaboration and remote processing for any kind of data. The service-oriented architecture is based on the cloud computing paradigm and relies on standard internet technologies to be efficient on a large panel of networks and clients. In this paper, we discuss the need of innovations in (i) pre and post processing visualization services, (ii) 3D large scientific data set scalable compression and transmission methods, (iii) collaborative virtual environments, and (iv) collaboration in multi-domains of CAD/E. We propose our open platform for collaborative simulation and scientific big data analysis. This platform is now available as an open project with all core components licensed under LGPL V2.1. We provide two examples of usage of the platform in CAD/E for sustainability engineering from one academic application and one industrial case study. Firstly, we consider chemical process engineering showing the development of a domain specific service. With the rise of global warming issues and with growing importance granted to sustainable development, chemical process engineering has turned to think more and more environmentally. Indeed, the chemical engineer has now taken into account not only the engineering and economic criteria of the process, but also its environmental and social performances. Secondly, an example of natural hazards management illustrates the efficiency of our approach for remote collaboration that involves big data exchange and analysis between distant locations. Finally we underline the platform benefits and we open our platform through next activities in innovation techniques and inventive design

On the Energy Efficiency and Performance of Irregular Application Executions on Multicore, NUMA and Manycore Platforms

International audienceUntil the last decade, performance of HPC architectures has been almost exclusively quantifiedby their processing power. However, energy efficiency is being recently considered as importantas raw performance and has become a critical aspect to the development of scalablesystems. These strict energy constraints guided the development of a new class of so-calledlight-weight manycore processors. This study evaluates the computing and energy performanceof two well-known irregular NP-hard problems — the Traveling-Salesman Problem (TSP) andK-Means clustering—and a numerical seismic wave propagation simulation kernel—Ondes3D—on multicore, NUMA, and manycore platforms. First, we concentrate on the nontrivial task ofadapting these applications to a manycore, specifically the novel MPPA-256 manycore processor.Then, we analyze their performance and energy consumption on those di↵erent machines.Our results show that applications able to fully use the resources of a manycore can have betterperformance and may consume from 3.8x to 13x less energy when compared to low-power andgeneral-purpose multicore processors, respectivel

Seismic Wave Propagation Simulations on Low-power and Performance-centric Manycores

International audienceThe large processing requirements of seismic wave propagation simulations make High Performance Computing (HPC) architectures a natural choice for their execution. However, to keep both the current pace of performance improvements and the power consumption under a strict power budget, HPC systems must be more energy e than ever. As a response to this need, energy-e and low-power processors began to make their way into the market. In this paper we employ a novel low-power processor, the MPPA-256 manycore, to perform seismic wave propagation simulations. It has 256 cores connected by a NoC, no cache-coherence and only a limited amount of on-chip memory. We describe how its particular architectural characteristics influenced our solution for an energy-e implementation. As a counterpoint to the low-power MPPA-256 architecture, we employ Xeon Phi, a performance-centric manycore. Although both processors share some architectural similarities, the challenges to implement an e seismic wave propagation kernel on these platforms are very di↵erent. In this work we compare the performance and energy e of our implementations for these processors to proven and optimized solutions for other hardware platforms such as general-purpose processors and a GPU. Our experimental results show that MPPA-256 has the best energy e consuming at least 77 % less energy than the other evaluated platforms, whereas the performance of our solution for the Xeon Phi is on par with a state-of-the-art solution for GPUs

Towards seismic wave modeling on heterogeneous many-core architectures using task-based runtime system

International audienceUnderstanding three-dimensional seismic wave propagation in complex media remains one of the main challenges of quantitative seismology. Because of its simplicity and numerical efficiency, the finite-differences method is one of the standard techniques implemented to consider the elastodynamics equation. Additionally, this class of modelling heavily relies on parallel architectures in order to tackle large scale geometries including a detailed description of the physics. Last decade, significant efforts have been devoted towards efficient implementation of the finite-differences methods on emerging architectures. These contributions have demonstrated their efficiency leading to robust industrial applications. The growing representation of heterogeneous architectures combining general purpose multicore platforms and accelerators leads to redesign current parallel application. In this paper, we consider StarPU task-based runtime system in order to harness the power of heterogeneous CPU+GPU computing nodes. We detail our implementation and compare the performance obtained with the classical CPU or GPU only versions. Preliminary results demonstrate significant speedups in comparison with the best implementation suitable for homogeneous cores

Contribution à la modélisation numérique de la propagation des ondes sismiques sur architectures multicœurs et hiérarchiques

En termes de prévention du risque associé aux séismes, la prédiction quantitative des phénomènes de propagation et d'amplification des ondes sismiques dans des structures géologiques complexes devient essentielle. Dans ce domaine, la simulation numérique est prépondérante et l'exploitation efficace des techniques de calcul haute performance permet d'envisager les modélisations à grande échelle nécessaires dans le domaine du risque sismique.Plusieurs évolutions récentes au niveau de l'architecture des machines parallèles nécessitent l'adaptation des algorithmes classiques utilisées pour la modélisation sismique. En effet, l'augmentation de la puissance des processeurs se traduit maintenant principalement par un nombre croissant de cœurs de calcul et les puces multicœurs sont maintenant à la base de la majorité des architectures multiprocesseurs. Ce changement correspond également à une plus grande complexité au niveau de l'organisation physique de la mémoire qui s'articule généralement autour d'une architecture NUMA (Non Uniform Memory Access pour accès mémoire non uniforme) de profondeur importante.Les contributions de cette thèse se situent à la fois au niveau algorithmique et numérique mais abordent également l'articulation avec les supports d'exécution optimisés pour les architectures multicœurs. Les solutions retenues sont validées à grande échelle en considérant deux exemples de modélisation sismique. Le premier cas se situe dans la préfecture de Niigata-Chuetsu au Japon (événement du 16 juillet 2007) et repose sur la méthode des différences finies. Le deuxième exemple met en œuvre la méthode des éléments finis. Un séisme hypothétique dans la région de Nice est modélisé en tenant compte du comportement non linéaire du sol.One major goal of strong motion seismology is the estimation of damage in future earthquake scenarios. Simulation of large scale seismic wave propagation is of great importance for efficient strong motion analysis and risk mitigation. Being particularly CPU-consuming, this three-dimensional problem makes use of high-performance computing technologies to make realistic simulation feasible on a regional scale at relatively high frequencies.Several evolutions at the chip level have an important impact on the performance of classical implementation of seismic applications. The trend in parallel computing is to increase the number of cores available at the shared-memory level with possible non-uniform cost of memory accesses. The increasing number of cores per processor and the effort made to overcome the limitation of classical symmetric multiprocessors SMP systems make available a growing number of NUMA (Non Uniform Memory Access) architecture as computing node. We therefore need to consider new approaches more suitable to such parallel systems.This PhD work addresses both the algorithmic issues and the integration of efficient programming models for multicore architectures. The proposed contributions are validated with two large scale examples. The first case is the modeling of the 2007 Niigata-Chuetsu, Japan earthquake based on the finite differences numerical method. The second example considers a potential seismic event in the Nice sedimentary basin in the French Riviera. The finite elements method is used and the nonlinear soil behavior is taken into account

Contribution à la modélisation numérique de la propagation des ondes sismiques sur architectures multicoeurs et hiérarchiques

One major goal of strong motion seismology is the estimation of damage in future earthquake scenarios. Simulation of large scale seismic wave propagation is of great importance for efficient strong motion analysis and risk mitigation. Being particularly CPU-consuming, this three-dimensional problem makes use of high-performance computing technologies to make realistic simulation feasible on a regional scale at relatively high frequencies. Several evolutions at the chip level have an important impact on the performance of classical implementation of seismic applications. The trend in parallel computing is to increase the number of cores available at the shared-memory level with possible non-uniform cost of memory accesses. The increasing number of cores per processor and the effort made to overcome the limitation of classical symmetric multiprocessors (SMP) systems make available a growing number of NUMA (Non Uniform Memory Access) architecture as computing node. We therefore need to consider new approaches more suitable to such parallel systems. This PhD work addresses both the algorithmic issues and the integration of efficient programming models for multicore architectures. The proposed contributions are validated with two large scale examples. The first case is the modeling of the 2007 Niigata-Chuetsu, Japan earthquake based on the finite differences numerical method. The second example considers a potential seismic event in the Nice sedimentary basin in the French Riviera. The finite elements method is used and the nonlinear soil behavior is taken into account.En termes de prévention du risque associé aux séismes, la prédiction quantitative des phénomènes de propagation et d'amplification des ondes sismiques dans des structures géologiques complexes devient essentielle. Dans ce domaine, la simulation numérique est prépondérante et l'exploitation efficace des techniques de calcul haute performance permet d'envisager les modélisations à grande échelle nécessaires dans le domaine du risque sismique. Plusieurs évolutions récentes au niveau de l'architecture des machines parallèles nécessitent l'adaptation des algorithmes classiques utilisées pour la modélisation sismique. En effet, l'augmentation de la puissance des processeurs se traduit maintenant principalement par un nombre croissant de coeurs de calcul et les puces multicoeurs sont maintenant à la base de la majorité des architectures multiprocesseurs. Ce changement correspond également à une plus grande complexité au niveau de l'organisation physique de la mémoire qui s'articule généralement autour d'une architecture NUMA (Non Uniform Memory Access pour accès mémoire non uniforme)~de profondeur importante. Les contributions de cette thèse se situent à la fois au niveau algorithmique et numérique mais abordent également l'articulation avec les supports d'exécution optimisés pour les architectures multicoeurs. Les solutions retenues sont validées à grande échelle en considérant deux exemples de modélisation sismique. Le premier cas se situe dans la préfecture de Niigata-Chuetsu au Japon (événement du 16 juillet 2007) et repose sur la méthode des différences finies. Le deuxième exemple met en oeuvre la méthode des éléments finis. Un séisme hypothétique dans la région de Nice est modélisé en tenant compte du comportement non linéaire du sol

Towards a cache-aware spacetime decomposition for the three-dimentional heat equation

International audienc