Ph.D
Group : Parallel Systems

Multi-Architectural Support: A Generic and Generative Approach

Starts on 23/09/2010
Advisor : ROZOY, Brigitte
[Joël FALCOU]

Funding : contrat doctoral du Ministère
Affiliation : Université Paris-Saclay
Laboratory : LRI

Defended on 20/06/2014, committee :

Research activities :

Abstract :
The constant increasing need for computing power has pushed the development of
parallel architectures. Scientific computing relies on the performance of such
architectures to produce scientific results. Programming efficient
applications that takes advantage of these computing systems remains a non
trivial task.

In this thesis, we present a new methodology to design architecture aware
software: the AA-DEMRAL methodology. This methodology aims at simplifying the
development of parallel programming tools with multi-architectural support
through a generic and generative approach.

We then present three high level programming tools that rely on this ap-
proach. First, we introduce the Boost.Dispatch library that provides a way to
develop software based on the AA-DEMRAL methodology. The Boost.Dispatch
library is a C++ generic framework for architecture aware function
dispatching. Then, we present two C++ template libraries implemented as
Architecture Aware DSELs which assess the AA-DEMRAL methodology through the
use of Boost.Dispatch: Boost.SIMD, that provides a high level API for SIMD
extensions and NT2 , which propose a Matlab like interface with support for
multi-core and SIMD based systems. We assess the performance of these
libraries and the validity of our new methodology through benchmarks.

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.