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Ph.D de

Ph.D
Group : Human-Centered Computing

Restructuration interactive des programmes

Starts on 16/09/2013
Advisor : HUOT, Stéphane
[BASTOUL Cédric]

Funding :
Affiliation : Université Paris-Sud
Laboratory : LRI-IN_SITU

Defended on 25/11/2016, committee :
Directeur de thèse :
Stéphane HUOT — directeur de recherche à Inria Lille

Co-directeur de thèse :
Cédric BASTOUL — professeur à l’université de Strasbourg

Rapporteurs :
- Stéphane CONVERSY — professeur à l’ENAC et Université de Toulouse
- Sanjay RAJOPADHYE — professeur à Colorado State University

Examinateurs :
- Jean-Daniel FEKETE — directeur de recherche à Inria Saclay
- Albert COHEN — directeur de recherche à Inria Paris et ENS Paris

Research activities :

Abstract :
Software development and program manipulation become increasingly complex with the massive adoption of parallel architectures, requiring significant expertise from developers. While numerous programming models and languages allow for creating efficient programs, they fall short at helping developers to restructure existing programs for more effective execution. At the same time, automatic approaches are overly conservative and imprecise to achieve a decent portion of the systems' performance without supplementary semantic information from the developer. To answer these challenges, we propose the interactive program restructuring approach, a bridge between semi-automatic program manipulation and software visualization. It is illustrated in this thesis by, first, extending a state-of-the-art polyhedral model for program representation so that it supports high-level program manipulation and, second, by designing and evaluating a direct manipulation visual interface for program restructuring. This interface provides information about the program that was not immediately accessible in the code and allows to manipulate programs without rewriting. We also propose a representation of an automatically computed program optimization in an understandable form, easily modifiable and reusable by the developer both visually and textually in a sort of human-machine partnership. To support different aspects of program restructuring, we design and evaluate a new interaction to communicate supplementary information, not critical for the task at hand. After an empirical study of developers' attention distribution when faced with visual and textual program representation, we discuss the implications for design of program manipulation tools in the instrumental interaction paradigm. We expect interactive program restructuring to make program manipulation for optimization more efficient and widely adopted.

Ph.D. dissertations & Faculty habilitations
DESIGN AND IMPLEMENTATION OF SOFTWARE TOOLS FOR PROGRAMMING ENVIRONMENTS AND FORMAL SPECIFICATIONS.


DISSéMINATION OPTIMALE DE L’INFORMATION DANS LES RéSEAUX


POWER-AWARE PROTOCOLS FOR WIRELESS SENSOR NETWORKS
In this thesis, we propose a formal energy model which allows an analytical study of energy consumption, for the first time in the context of population protocols. Population protocols model one special kind of sensor networks where anonymous and uniformly bounded memory sensors move unpredictably and communicate in pairs. To illustrate the power and the usefulness of the proposed energy model, we present formal analyses on time and energy, for the worst and the average cases, for accomplishing the fundamental task of data collection. Two power-aware population protocols, (deterministic) EB-TTFM and (randomized) lazy-TTF, are proposed and studied for two different fairness conditions, respectively. Moreover, to obtain the best parameters in lazy-TTF, we adopt optimization techniques and evaluate the resulting performance by experiments. Then, we continue the study on optimization for the poweraware data collection problem in wireless body area networks. A minmax multi-commodity netow formulation is proposed to optimally route data packets by minimizing the worst power consumption. Then, a variable neighborhood search approach is developed and the numerical results show its efficiency. At last, a stochastic optimization model, namely the chance constrained semidefinite programs, is considered for the realistic decision making problems with random parameters. A novel simulation-based algorithm is proposed with experiments on a real control theory problem. We show that our method allows a less conservative solution, than other approaches, within reasonable time.