nuACM
5 HN recommenders · recommended in 10 months, 2010–2017
1:19:57 · 28K views · Apr 23, 2010
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.
Recommended as a fascinating look at easy parallelism abstractions, though other commenters note the approach never beat sequential C by much.
11 comments from Hacker News
This is both a straw man (OP asked for limited help, not omnipotence), and predicting the future by drawing a line from the past. Functional compilers that outperform hand tuned C++ on many core machines may be possible, but very hard. Just because everyone has previously underestimated the difficulty does not mean it's an idea worthy of derision. I think it's much more likely that it's perpetually ten years away, until one day it's not (like the current, possibly temporary end to Moore's Law). Watching Simon Peyton Jones' talk on data parallel Haskell, I'd not make a bet against high performance Haskell's eventual success, especially as core counts continue to climb, and NUMA gets more NU. this video
I believe Haskell compilers are pretty amenable to stuff like this - watching "Data Parallel Haskell", it was interesting to see how they actually map out contiguous memory, etc. this video Sadly, it doesn't seem to be as tightly integrated as what you'd see in SBCL from a developers PoV, but I'm certainly intrigued by their ability to achieve some very specific implementation while separating it from the expression of the algorithms.
Here's the main Haskell hero, Simon Peyton Jones giving a talk about data parallel Haskell, which I think is a promising language abstraction to do hard things with parallelism relatively easily: this video As for techniques that can be used now: Haskell's `par` and `pseq` combinators allow adding parallelization to "hard" code after-the-fact pretty easily.
While this isn't undeniable proof, it's interesting none the less, Simon Peyton Jones: Data Parallel Haskell. this video He talks about how this scheme can parallelize non-trivial data structures, and how tedious book keeping can be avoided. I haven't used it yet in a project, so I can't speak of its efficacy - but worth a watch, imho.
Well nested data parallelism generally applies to hierarchical structures like trees, whereas flat data parallelism applies to things like arrays and whatnot. You might want to watch this (similar in some ways) talk by SPJ on Data Parallel Haskell this video