Ph.D
Group : Parallelism

Methods and algorithms for solving linear systems of equations on massively parallel computers

Starts on 09/01/2008
Advisor : GRIGORI, Laura

Funding : CDD sur contrat INRIA
Affiliation : Université Paris-Saclay
Laboratory : LRI

Defended on 07/03/2012, committee :

Research activities :

Abstract :
Multicore processors are considered to be nowadays the future of computing, and they will have an important impact in scientific computing. In this thesis, we study methods and algorithms for solving efficiently sparse and dense large linear systems on future petascale machines. Due to the increasing communication cost compared to the time the processors take to perform arithmetic operations, our approach embrace the communication avoiding algorithm principle by doing some redundant computations and uses several adaptations to achieve better performance on multicore machines.

We decompose the problem to solve into several phases that would be then designed or optimized separately. In the first part, we present an algorithm based on hypergraph partitioning and which considerably reduces the fill-in incurred in the LU factorization of sparse unsymmetric matrices. In the second part, we present two communication avoiding algorithms for computing the LU and QR factorisations that are adapted to multicore environments. The main contribution of this part is to reorganize the computations such as to reduce bus contention and using efficiently resources. Then, we extend this work for clusters of multi-core processors. In the third part, we present the use of a scheduling and optimization strategy that uses hybrid static/dynamic scheduling of the task dependency graph. This strategy provides a balance of data locality, load balancing, reduces the overhead, and can adapt to dynamic changes in the system.

Our results obtained on several architectures show that all our algorithms are efficient and lead to significant performance gains. We can achieve from 30 up to 110% improvement over the corresponding routines of our algorithms in well known libraries.

CAUSAL LEARNING FOR DIAGNOSTIC SUPPORT


CAUSAL UNCERTAINTY QUANTIFICATION UNDER PARTIAL KNOWLEDGE AND LOW DATA REGIMES


MICRO VISUALIZATIONS: DESIGN AND ANALYSIS OF VISUALIZATIONS FOR SMALL DISPLAY SPACES
The topic of this habilitation is the study of very small data visualizations, micro visualizations, in display contexts that can only dedicate minimal rendering space for data representations. For several years, together with my collaborators, I have been studying human perception, interaction, and analysis with micro visualizations in multiple contexts. In this document I bring together three of my research streams related to micro visualizations: data glyphs, where my joint research focused on studying the perception of small-multiple micro visualizations, word-scale visualizations, where my joint research focused on small visualizations embedded in text-documents, and small mobile data visualizations for smartwatches or fitness trackers. I consider these types of small visualizations together under the umbrella term ``micro visualizations.'' Micro visualizations are useful in multiple visualization contexts and I have been working towards a better understanding of the complexities involved in designing and using micro visualizations. Here, I define the term micro visualization, summarize my own and other past research and design guidelines and outline several design spaces for different types of micro visualizations based on some of the work I was involved in since my PhD.