Study of the distribution on a large scale system of numerical computing processing compressed sparse matrices
type: Dataset
description: The treatment of sparse numerical problems on large scale systems is often reduced to that of their kernels. For reasons of efficiency in time and space, specific compressing formats are used for storing the matrices of these problems. Most of sparse scientific computations are led to linear algebra problems. Here two fundamental problems are often considered: linear systems resolution and eigenvalue computation. In this thesis, we address the study of distribution of computations performed in iterative methods to solve such problems. The sparse matrix-vector product (SMVP) constitutes a basic kernel in such iterative methods. Thus, our problem reduces to the study of SMVP distribution on large scale distributed systems. Three phases are required for achieving our objectives (i)- pre-processing, (ii)- processing and (iii)- postprocessing. In phase 1, we first process the optimization of four versions of the SMVP algorithm corresponding to four specific matrix compressing formats; we study then their performances on sequential target machines. In addition, we focus on the study of load balancing in the procedure of data distribution (i.e. the sparse matrix rows) on a large scale distributed system. Concerning the processing phase, it consists in validating our study by a series of experimentations achieved on a volunteer distributed system that we installed through using XtremWeb-CH middleware. As to the post-processing phase, it consists in interpreting the experimental results previously obtained in order to deduce adequate conclusions
issued: 2026-05-20T05:05:42.530102
language: fr
modified: 2026-05-20T05:05:42.530108
contact point: Organization
  fn: CCSD
keyword: algorithm-optimization
keyword: compressing-format
keyword: equilibrage-de-charge
keyword: format-de-compression
keyword: infoeu-reposemanticsdoctoralthesis
keyword: infoinfo-dccomputer-science-csdistributed-parallel-and-cluster-computing-csdc
keyword: large-scale-distributed-system
keyword: load-balancing
keyword: matrice-creuse
keyword: optimisation-dalgorithme
keyword: produit-matrice-vecteur-creux
keyword: sparse-matrix
keyword: sparse-matrix-vector-product
keyword: systeme-distribue-a-grande-echelle
keyword: theses
landing page: https://theses.hal.science/tel-00693322

Study of the distribution on a large scale system of numerical computing processing compressed sparse matrices
type: Distribution
format: HTML
issued: 2026-05-20T05:05:42.624465
modified: 2026-05-20T05:05:42.502845
access URL: https://theses.hal.science/tel-00693322

test_moissonnage_selune
type: Agent