Ph.D
Group : Verification of Algorithms, Languages and Systems

Verification via Model Checking of Parameterized Concurrent Programs on Weak Memory Models

Starts on 01/10/2014
Advisor : ZAIDI, Fatiha

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

Defended on 24/09/2018, committee :

Research activities :

Abstract :
Modern multiprocessors and microprocesseurs implement weak or relaxed memory models, in which the apparent order of memory operation does not follow the sequential consistency (SC) proposed by Leslie Lamport. Any concurrent program running on such architecture and designed with an SC model in mind may exhibit new behaviors during its execution, some of which may potentially be incorrect. For instance, a mutual exclusion algorithm, correct under an interleaving semantics, may no longer guarantee mutual exclusion when implemented on a weaker architecture.

Reasoning about the semantics of such programs is a difficult task. Moreover, most concurrent algorithms are designed for an arbitrary number of processus. We would like to ensure the correctness of concurrent algorithms, regardless of the number of processes involved. For this purpose, we rely on the Model Checking Modulo Theories (MCMT) framework, developed by Ghilardi and Ranise, which allows for the verification of safety properties of parameterized concurrent programs, that is to say, programs involving an arbitrary number of processes. We extend this technology with a theory for reasoning about weak memory models. The result of this work is an extension of the Cubicle model checker called Cubicle-W, which allows the verification of safety properties of parameterized transition systems running under a weak memory model similar to TSO.

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.