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Simon Peyton Jones: Data Parallel Haskell

nuACM · talk · Watch on YouTube

What it is

Simon Peyton Jones explains nested data parallelism and Data Parallel Haskell, an approach to exploiting multicore and distributed hardware while keeping a simple single-control-flow programming model.

Why Hacker News recommends it

Recommended as a fascinating look at easy parallelism abstractions, though other commenters note the approach never beat sequential C by much.

  • 5 people recommended it
  • 5 different months
  • 0 strong recommendations
  • first 2010, last 2017

What HN commenters said

  1. “but worth a watch, imho”

    Parallelizing non-trivial data structures while avoiding tedious bookkeeping.

    drhodes, 2010-06 · Recommendation · in “SeaMicro drops an atom bomb on the server industry”

  2. “a promising language abstraction to do hard things with parallelism relatively easily”

    Data parallel Haskell as an abstraction for easy parallelism.

    Peaker, 2011-04 · Recommendation · in “Joel Spolsky: Can your programming language do this?”

  3. “I'd not make a bet against high performance Haskell's eventual success”

    Makes high performance Haskell's eventual success look plausible.

    reitzensteinm, 2014-04 · Recommendation · in “Speed memory access by arranging data to take advantage of CPU caching”

  4. “it was interesting to see how they actually map out contiguous memory”

    Shows how Haskell compilers map out contiguous memory.

    sgrove, 2014-09 · Recommendation · in “Rhine – A Lisp on LLVM”

  5. “You might want to watch this (similar in some ways) talk by SPJ on Data Parallel Haskell”

    Similar talk on nested data parallelism for hierarchical structures.

    anaphor, 2014-11 · Recommendation · in “Guy Steele on Functional Code for Parallel Execution (2009) [video]”

Excerpts from Hacker News comments, linked to the originals. Summaries and labels were written by a language model from those comments.

Topics: haskell, nested data parallelism, multicore, simon peyton jones, compilers

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