Physique numérique

La physique numérique (ou parfois physique informatique) est l'étude et l'implémentation d'algorithmes numériques dans le but de résoudre des problèmes physiques pour lesquels une théorie existe déjà. Elle est souvent considérée comme une sous-discipline de la physique théorique mais certains la considèrent comme une branche intermédiaire entre la physique théorique et la physique expérimentale. En général, les physiciens définissent un système et son évolution grâce à des formules mathématiques précises. Il arrive souvent que la solution des équations basées sur les principes de la physique fondamentale ne soit pas adaptée à la description du système. Ceci est particulièrement vrai dans le cas de la mécanique quantique, où seulement une poignée de modèles simples possèdent des solutions analytiques complètes. Dans les cas où les systèmes ont seulement des solutions numériques, des calculs numériques sont employés. Domaines d'application Les calculs numériques sont employé

Analytic performance modeling and analysis of detailed neuron simulations

Francesco Cremonesi, Gerhard Wellein

Big science initiatives are trying to reconstruct and model the brain by attempting to simulate brain tissue at larger scales and with increasingly more biological detail than previously thought possible. The exponential growth of parallel computer performance has been supporting these developments, and at the same time maintainers of neuroscientific simulation code have strived to optimally and efficiently exploit new hardware features. Current state-of-the-art software for the simulation of biological networks has so far been developed using performance engineering practices, but a thorough analysis and modeling of the computational and performance characteristics, especially in the case of morphologically detailed neuron simulations, is lacking. Other computational sciences have successfully used analytic performance engineering, which is based on "white-box," that is, first-principles performance models, to gain insight on the computational properties of simulation kernels, aid developers in performance optimizations and eventually drive codesign efforts, but to our knowledge a model-based performance analysis of neuron simulations has not yet been conducted. We present a detailed study of the shared-memory performance of morphologically detailed neuron simulations based on the Execution-Cache-Memory performance model. We demonstrate that this model can deliver accurate predictions of the runtime of almost all the kernels that constitute the neuron models under investigation. The gained insight is used to identify the main governing mechanisms underlying performance bottlenecks in the simulation. The implications of this analysis on the optimization of neural simulation software and eventually codesign of future hardware architectures are discussed. In this sense, our work represents a valuable conceptual and quantitative contribution to understanding the performance properties of biological networks simulations.

2020

Computational Approach to the Geometry of Compact Riemann Surfaces

Manuel Racle

The goal of this document is to provide a generalmethod for the computational approach to the topology and geometry of compact Riemann surfaces. The approach is inspired by the paradigms of object oriented programming. Our methods allow us in particular to model, for numerical and computational purposes, a compact Riemann surface given by Fenchel-Nielsen parameters with respect to an arbitrary underlying graph, this in a uniformand robust manner. With this programming model established we proceed by proposing an algorithmthat produces explicit compact fundamental domains of compact Riemann surfaces as well as generators of the corresponding Fuchsian groups. In particular, we shall explain how onemay obtain convex geodesic canonical fundamental polygons. In a second part we explain in what manner simple closed geodesics are represented in our model. This will lead us to an algorithm that enumerates all these geodesics up to a given prescribed length. Finally, we shall briefly overview a number of possible applications of our method, such as finding the systoles of a Riemann surface, or drawing its Birman-Series set in a fundamental domain.

EPFL2013

Analytic performance modeling and analysis of detailed neuron simulations

Francesco Cremonesi, Gerhard Wellein

2020

A computational approach to image retrieval

Analytic performance modeling and analysis of detailed neuron simulations

Computational Approach to the Geometry of Compact Riemann Surfaces

Computational Approach to the Geometry of Compact Riemann Surfaces

Analytic performance modeling and analysis of detailed neuron simulations

A computational approach to image retrieval