Formal analysis techniques for gossiping protocols

We give a survey of formal verification techniques that can be used to corroborate existing experimental results for gossiping protocols in a rigorous manner. We present properties of interest for gossiping protocols and discuss how various formal evaluation techniques can be employed to predict them

Overcoming extreme-scale reproducibility challenges through a unified, targeted, and multilevel toolset

pre-printReproducibility, the ability to repeat program executions with the same numerical result or code behavior, is crucial for computational science and engineering applications. However, non-determinism in concurrency scheduling often hampers achieving this ability on high performance computing (HPC) systems. To aid in managing the adverse effects of non-determinism, prior work has provided techniques to achieve bit-precise reproducibility, but most of them focus only on small-scale parallelism. While scalable techniques recently emerged, they are disparate and target special purposes, e.g., single-schedule domains. On current systems with O(106) compute cores and future ones with O(109), any technique that does not embrace a unied, targeted, and multilevel approach will fall short of providing reproducibility. In this paper, we argue for a common toolset that embodies this approach, where programmers select and compose complementary tools and can effectively, yet scalably, analyze, control, and eliminate sources of non-determinism at scale. This allows users to gain reproducibility only to the levels demanded by specific code development needs. We present our research agenda and ongoing work toward this goal

Overcoming extreme-scale reproducibility challenges through a unified, targeted, and multilevel toolset

Abstract not provide

Abstraction-Based Data-Driven Control

Our world is living a paradigm shift in technology policy, often referred to as the Cyber-Physical Revolution or Industry 4.0. Nowadays, Cyber-Physical Systems are ubiquitous in modern control engineering, including automobiles, aircraft, building control systems, chemical plants, transportation systems, and so on. The interactions of the physical processes with the machines that control them are becoming increasingly complex, and in a growing number of situations either the model of the system is unavailable, or it is too difficult to describe accurately. Therefore, embedded computers need to "learn" the optimal way to control the systems by the mere observation of data. What seems the best approach to control these complex systems is often by discretizing the different variables, thus transforming the model into a combinatorial problem on a finite-state automaton, which is called an abstraction of the real system. Until now, this approach, often referred to as "abstraction-based control" or "symbolic control", has not been proved useful beyond small academic examples. In this project I aim to show the potential of this approach by implementing a novel data-driven approach based on a probabilistic interpretation of the discretization error. I have developed a toolbox (github.com/davidedl-ucl/master-thesis) implementing this kind of control with the aim of integrating it in the Dionysos software github.com/dionysos-dev). With this software, I succeeded in efficiently solving problems for non-linear control systems such as a path planning for an autonomous vehicle and a cart-pole balancing problem. The long-term objective of this project is to improve the methods implemented in my current software by employing a variable discretization of the state space and to consider complex specifications such as LTL formulas.Our world is living a paradigm shift in technology policy, often referred to as the Cyber-Physical Revolution or Industry 4.0. Nowadays, Cyber-Physical Systems are ubiquitous in modern control engineering, including automobiles, aircraft, building control systems, chemical plants, transportation systems, and so on. The interactions of the physical processes with the machines that control them are becoming increasingly complex, and in a growing number of situations either the model of the system is unavailable, or it is too difficult to describe accurately. Therefore, embedded computers need to "learn" the optimal way to control the systems by the mere observation of data. What seems the best approach to control these complex systems is often by discretizing the different variables, thus transforming the model into a combinatorial problem on a finite-state automaton, which is called an abstraction of the real system. Until now, this approach, often referred to as "abstraction-based control" or "symbolic control", has not been proved useful beyond small academic examples. In this project I aim to show the potential of this approach by implementing a novel data-driven approach based on a probabilistic interpretation of the discretization error. I have developed a toolbox (github.com/davidedl-ucl/master-thesis) implementing this kind of control with the aim of integrating it in the Dionysos software github.com/dionysos-dev). With this software, I succeeded in efficiently solving problems for non-linear control systems such as a path planning for an autonomous vehicle and a cart-pole balancing problem. The long-term objective of this project is to improve the methods implemented in my current software by employing a variable discretization of the state space and to consider complex specifications such as LTL formulas