BABOULIN Marc
Faculty habilitation
Group : Parallelism

Fast and reliable solutions for numerical linear algebra solvers in high-performance computing.

Starts on 05/12/2012
Advisor :

Funding :
Affiliation : Université Paris-Saclay
Laboratory : LRI

Defended on 05/12/2012, committee :

Research activities :

Abstract :
Recent years have seen an increase in peak ``local" speed through
parallelism in terms of multicore processors and GPU accelerators. At
the same time, the cost of communication between memory hierarchies
and/or between processors have become a major bottleneck for most
linear algebra algorithms.
We first explain how hybrid multicore+GPU systems can be used
efficiently to enhance performance of linear algebra libraries. We
illustrate this approach by considering hybrid factorizations where we
split the computation over a multicore and a graphic processor and
where the amount of communication is significantly reduced.
Next we describe a class of randomized algorithms that accelerate
factorization of general or symmetric indefinite systems on multicore
or hybrid multicore+GPU systems. Randomization prevents the
communication overhead due to pivoting, is computationally inexpensive
and requires very little storage. The resulting solvers outperform
existing routines while providing us with a satisfying accuracy.
Finally we present numerical tools that enable us to assess the
quality of the computed solution of overdetermined linear least
squares. Our method is based on deriving exact values or statistical
estimates for the condition number of these problems. We describe
algorithms and software to compute these quantities using HPC
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.