sml-parseq
futhark
| sml-parseq | futhark | |
|---|---|---|
| 1 | 52 | |
| 4 | 2,293 | |
| - | 1.8% | |
| 4.5 | 9.8 | |
| about 3 years ago | 5 days ago | |
| Standard ML | Haskell | |
| MIT License | ISC License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
sml-parseq
-
Provably Space-Efficient Parallel Functional Programming
Author here. One of the cool things about the property we're using here---disentanglement---is that it specifically allows for shared data, under only mild restrictions. This allows us, for example, to implement fast libraries which utilize shared mutable state for efficiency under the hood. A good example is our parallel arrays library (https://github.com/shwestrick/sml-parseq), which is "purely functional" in terms of its interface, but not its implementation.
It's helpful here to distinguish parallelism from concurrency. Disentanglement naturally emerges in data-race-free parallel programs, which have no concurrency. But certainly, you bring up a good point for programs that are highly concurrent in addition to being highly parallel. There's lots of work already on concurrent functional programming, for example CML (https://en.wikipedia.org/wiki/Concurrent_ML), and we think these ideas could be adapted to work with disentanglement really well.
futhark
-
What downsides exist to Futhark? Seems almost too good to be true?
Why Futhark? (futhark-lang.org)
-
GPU Programming: When, Why and How?
There is no on-going work to support Metal apart from the work done by Miles. There's an old issue about it: https://github.com/diku-dk/futhark/issues/853#issuecomment-5...
-
Is Parallel Programming Hard, and, If So, What Can You Do About It? v2023.06.11a
Functional programming can be a great way to handle parallel programming in a sane way. See the Futhark language [1], for example, that accepts high-level constructs like map and convert them to the appropriate machine code, either on the CPU or the GPU.
[1] https://futhark-lang.org/
-
Is there a programming language that will blow my mind?
Futhark - use a functional language to program the gpu
-
Does This Language Exist?
You might want to look into Futhark, although it's mainly designed for writing GPU code.
- Learn WebGPU
-
Two-tier programming language
Futhark https://futhark-lang.org/
- Best book on writing an optimizing compiler (inlining, types, abstract interpretation)?
- Functional GPU programming: what are alternatives or generalizations of the idea of "number of cycles must be known at compile time"?
- APL: An Array Oriented Programming Language (2018)
What are some alternatives?
sml-analyzer - An experimental language server for SomewhatML
arrayfire-rust - Rust wrapper for ArrayFire
apltail - APL Compiler targeting a typed array intermediate language
dex-lang - Research language for array processing in the Haskell/ML family
LunarML - The Standard ML compiler that produces Lua/JavaScript
Halide - a language for fast, portable data-parallel computation
mlton - The MLton repository
julia - The Julia Programming Language
install-mlkit - Action for installing MLKit
BQN - An APL-like programming language. Self-hosted!
smlnj-viscomp-example - An example of how to use SML/NJ's Visible Compiler APIs
kompute - General purpose GPU compute framework built on Vulkan to support 1000s of cross vendor graphics cards (AMD, Qualcomm, NVIDIA & friends). Blazing fast, mobile-enabled, asynchronous and optimized for advanced GPU data processing usecases. Backed by the Linux Foundation.