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Project: Data-Parallel Interpretation of Numerical Code hos Københavns Universitet

Programming Languages and Theory of Computation

Denne sektion udfører forskning i programmeringssprogsteknologi og i teorien om beregning. Meget af arbejdet involverer emner i krydsfeltet mellem programmeringssprog teori og anvendelse.

Forskningssektionen er inddelt i otte områder:

  • Decentraliseret systemer
  • Sikkerhed og privatliv
  • Higher-Order and Typed Programming Language
  • Programinversion og reversibel beregning
  • Programmeringssprog teori og teknologi
  • Teori om beregning
  • Funktionel teknologi for moderne arkitektur
  • Finansiel gennemsigtighed

Læs mere her: https://di.ku.dk/english/research/pltc/

 

Engelsk version:

The section perform research in programming language technology and in the theory of computation. Much of the work involves topics in the intersection of programming language theory and applications.

The section is divided into eight areas:

  • Decentralized Systems
  • Security and Privacy
  • Higher-Order and Typed Programming Language
  • Program Inversion and Reversible Computing
  • Programmering Language Theory and Technology
  • Theory of Computation
  • Functional Technology for Modern Architectures
  • Financial Transparency

Read more here: https://di.ku.dk/english/research/pltc/

Project idea:

Sometimes, it is beneficial to run in parallel (e.g., on a GPU) code that is represented as input data (e.g., instructions for a stack machine, perhaps generated by a Python program).

Using Futhark's support for simple algebraic (non-recursive) datatypes, this project aims at investigating the possibility for parallel execution of sequences of stack machine instructions
and to investigate possible uses of the technique.

Possible use cases include analysis of fractal images and recurrent equations.

A project may involve identifying a real-world problem to solve, for instance within economics or engineering, and it will involve getting acquainted with using the data-parallel programming language

Futhark, which makes it possible to run data-parallel applications
efficiently on GPUs.

Supervisor:
Martin Elsman, mael@di.ku.dk

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