University of Illinois Urbana-Champaign

Programming systems for safe and accurate parallel programs in the face of uncertainty

Fernando, Vimuth

Content Files
FERNANDO-DISSERTATION-2022.pdf

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https://hdl.handle.net/2142/116244

Description

Title
Programming systems for safe and accurate parallel programs in the face of uncertainty
Author(s)
Fernando, Vimuth
Issue Date
2022-07-14
Director of Research (if dissertation) or Advisor (if thesis)
Misailovic, Sasa
Doctoral Committee Chair(s)
Misailovic, Sasa
Committee Member(s)
  • Torrellas, Josep
  • Mitra, Sayan
  • Carbin, Micheal
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
  • distributed programming
  • wireless network-on-chip
  • reliability and accuracy
  • approximate programming
Abstract
Many emerging distributed applications operate on inherently noisy data or produce approximate results. Emerging application domains, including IoT, self-driving cars, and precision agriculture, routinely need to deal with noise from their sensors and unreliable communication mediums. Furthermore, increased volume of data, and the rise of highly parallel and often heterogeneous systems have brought forth new challenges in overcoming bottlenecks in both computation and communication between processing units. Many prominent systems adopted approximation in communication to address these challenges. Developing software in the presence of these novel architectures, optimizations, and approximations can be a challenging task. As these systems get deployed in safety critical situations, it is important to verify that they behave in a predictable and safe manner, even in situations where the outcomes are uncertain. Developers need to ensure that the programs operating with noisy data do not result in unexpected crashes and produce acceptable results with high reliability. In recent years, researchers have designed several analyses for verifying these program properties in the presence of uncertainty. These prior works had stayed away from parallel programming models, in part due to the complexities involved with reasoning about parallel interactions. This dissertation presents an ecosystem of several programming language tools and techniques across the computational stack that provides foundations for safety and accuracy analyses of parallel programs that deal with uncertainty. First, the dissertation will present a software infrastructure that enables simple and efficient use of a novel architecture that speeds up communication in a manycore processor using a wireless network. Next, the dissertation will show how to use programming language techniques to reduce the complexity of verifying the correctness of a subset of asynchronous message passing parallel programs in Parallely. We show how to lift many existing analyses that are designed for sequential programs to the domain of parallel programs. Next, the dissertation presents how to further extend verification to bigger programs and newer error models using runtime monitoring in Diamont. Finally, the dissertation presents several case studies that look at extending runtime verification to recovery mechanisms, algorithmic fairness analysis, and a novel architecture with potentially erroneous wireless communication.
Graduation Semester
2022-08
Type of Resource
Thesis
Handle URL
https://hdl.handle.net/2142/116244
Copyright and License Information
Copyright 2022 Vimuth Fernando

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