Ph.D
Group : Parallel Systems

Méthodes de génération automatique de code appliquées à l’algèbre linéaire numérique dans le calcul haute performance

Starts on 01/10/2013
Advisor : BABOULIN, Marc

Funding : Contrat doctoral uniquement recherche
Affiliation : Université Paris-Saclay
Laboratory : LRI PARSYS

Defended on 26/09/2016, committee :

Research activities :

Abstract :
Parallelism in today’s computer architectures is ubiquitous whether it be in supercomputers, workstations or on portable devices such as smartphones. Exploiting efficiently these systems for a specific application requires a multidisciplinary effort that concerns Domain Specific Languages (DSL), code generation and optimization techniques as well as application-specific numerical algorithms.
In this PhD thesis, we present a method of high level programming that takes into account the features of heterogeneous architectures and the properties of matrices to build a generic dense linear algebra solver. As GPUs have become an asset in high performance computing, incorporating their use in general solvers is an important issue.
We extend our approach to a new multistage programming model that alleviates the interoperability problems between the CPU and GPU programming models.
Our multistage approach is used to automatically generate GPU code for CPU-based
element-wise expressions and parallel skeletons while allowing for type-safe program generation.
Finally, we investigate how to apply high level programming techniques to batched
computations and tensor contractions. We first explain how to design a simple data container
using modern C++-14 programming techniques. Then, we study the issues around batched
computations, memory locality and code vectorization to implement a highly optimized
matrix-matrix product for small sizes using SIMD instructions.

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.