Accelerate
Data.Array.Accelerate is an embedded language in Haskell for high-performance computing using multi-dimensional arrays. It supports collective operations like maps, reductions, and permutations, which can be compiled and executed on various architectures including GPUs. The library includes backends for CPUs and CUDA-enabled GPUs, along with tools for interoperability and performance optimization.
- ▪Accelerate enables high-performance array computations in Haskell through online compilation.
- ▪It supports parallel execution on multicore CPUs and NVIDIA GPUs with compute capability 3.0 or higher.
- ▪The library provides multiple add-on packages for I/O, FFT, BLAS/LAPACK operations, image processing, and random number generation.
- ▪Examples and tutorials are available in Simon Marlow's book and in Trevor's PhD thesis.
- ▪Accelerate is open-source, available on Hackage and GitHub, with documentation and test suites included.
Hacker News (Front Page) files mainly under programming. We currently carry 996 of its stories. Top-voted stories on Hacker News.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/AccelerateHS/accelerate |
| Publication time | Sat, 16 May 2026 13:42:34 +0000 |
| Retrieval time | 2026-05-16T14:15:18.675Z |
| Last seen | 2026-05-16T14:15:18.675Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | None |
| Cluster logic | Not yet clustered, or no peer story found in the clustering window. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
High-performance parallel arrays for Haskell Data.Array.Accelerate defines an embedded language of array computations for high-performance computing in Haskell. Computations on multi-dimensional, regular arrays are expressed in the form of parameterised collective operations (such as maps, reductions, and permutations). These computations are online-compiled and executed on a range of architectures. For more details, see our papers: Accelerating Haskell Array Codes with Multicore GPUs Optimising Purely Functional GPU Programs (slides) Embedding Foreign Code Type-safe Runtime Code Generation: Accelerate to LLVM (slides) (video) Streaming Irregular Arrays (video) There are also slides from some presentations on Accelerate: Embedded Languages for High-Performance Computing in Haskell GPGPU…
Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.