Memory-Region Aware Task-Parallel Programming in Standard ML 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:

This project aims at investigating the use of MLKit (a Standard ML compiler that uses regioninference as the primary memory management strategy) for implementing a set of taskparallel algorithms.

Combining a memory discipline such as region-inference with routines for task-parallelism can be beneficial due to guarantees given by the memory discipline that data-races cannot
appear. The MLKit compiler, developed at DIKU, has recently been augmented with preliminary support for writing task-parallel programs. This project aims at experimenting with
and evaluating this compiler extension and perhaps compare its performance with the MPL compiler, a derivative of MLton.

A project may involve identifying a real-world problem to solve, for instance within economics or engineering, and it will involve getting acquainted with writing programs in Standard ML (much similar to writing F# or OCaml code).

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

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