{"id":13787707,"url":"https://github.com/AccelerateHS/accelerate","last_synced_at":"2025-05-12T01:31:33.190Z","repository":{"id":37587620,"uuid":"1585601","full_name":"AccelerateHS/accelerate","owner":"AccelerateHS","description":"Embedded language for high-performance array computations","archived":false,"fork":false,"pushed_at":"2025-04-24T21:17:54.000Z","size":16150,"stargazers_count":917,"open_issues_count":64,"forks_count":123,"subscribers_count":41,"default_branch":"master","last_synced_at":"2025-05-03T07:14:02.388Z","etag":null,"topics":["accelerate","cuda","gpu","gpu-computing","hacktoberfest","haskell","llvm","parallel-computing"],"latest_commit_sha":null,"homepage":"https://www.acceleratehs.org","language":"Haskell","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/AccelerateHS.png","metadata":{"files":{"readme":"README.md","changelog":"CHANGELOG.md","contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2011-04-08T02:50:11.000Z","updated_at":"2025-04-30T19:30:44.000Z","dependencies_parsed_at":"2022-07-16T20:00:39.612Z","dependency_job_id":"344cb2f4-85c1-4bf2-b396-77a933ef8211","html_url":"https://github.com/AccelerateHS/accelerate","commit_stats":{"total_commits":2982,"total_committers":42,"mean_commits":71.0,"dds":"0.39403085177733066","last_synced_commit":"237303a660a41f04e43b1661c3fa31528be7927b"},"previous_names":[],"tags_count":20,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AccelerateHS%2Faccelerate","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AccelerateHS%2Faccelerate/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AccelerateHS%2Faccelerate/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AccelerateHS%2Faccelerate/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/AccelerateHS","download_url":"https://codeload.github.com/AccelerateHS/accelerate/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":253659366,"owners_count":21943626,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["accelerate","cuda","gpu","gpu-computing","hacktoberfest","haskell","llvm","parallel-computing"],"created_at":"2024-08-03T21:00:27.557Z","updated_at":"2025-05-12T01:31:32.698Z","avatar_url":"https://github.com/AccelerateHS.png","language":"Haskell","readme":"\u003cdiv align=\"center\"\u003e\n\u003cimg width=\"450\" src=\"https://github.com/AccelerateHS/accelerate/raw/master/images/accelerate-logo-text-v.png?raw=true\" alt=\"henlo, my name is Theia\"/\u003e\n\n# High-performance parallel arrays for Haskell\n\n[![CI](https://github.com/tmcdonell/accelerate/actions/workflows/ci.yml/badge.svg)](https://github.com/tmcdonell/accelerate/actions/workflows/ci.yml)\n[![Gitter](https://img.shields.io/gitter/room/nwjs/nw.js.svg)](https://gitter.im/AccelerateHS/Lobby)\n[![Hackage](https://img.shields.io/hackage/v/accelerate.svg)](https://hackage.haskell.org/package/accelerate)\n[![Stackage LTS](https://stackage.org/package/accelerate/badge/lts)](https://stackage.org/lts/package/accelerate)\n[![Stackage Nightly](https://stackage.org/package/accelerate/badge/nightly)](https://stackage.org/nightly/package/accelerate)\n\n\u003c/div\u003e\n\n`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.\n\nFor more details, see our papers:\n\n * [Accelerating Haskell Array Codes with Multicore GPUs][CKLM+11]\n * [Optimising Purely Functional GPU Programs][MCKL13] ([slides][MCKL13-slides])\n * [Embedding Foreign Code][CMCK14]\n * [Type-safe Runtime Code Generation: Accelerate to LLVM][MCGN15] ([slides][MCGN15-slides]) ([video][MCGN15-video])\n * [Streaming Irregular Arrays][CMCK17] ([video][CMCK17-video])\n\nThere are also slides from some fairly recent presentations:\n\n * [Embedded Languages for High-Performance Computing in Haskell][Embedded]\n * [GPGPU Programming in Haskell with Accelerate][YLJ13-slides] ([video][YLJ13-video]) ([workshop][YLJ13-workshop])\n\nChapter 6 of Simon Marlow's book [Parallel and Concurrent Programming in Haskell][Mar13] contains a tutorial introduction to Accelerate.\n\n[Trevor's PhD thesis][Trevor-thesis] details the design and implementation of frontend optimisations and CUDA backend.\n\n\n**Table of Contents**\n\n- [An Embedded Language for Accelerated Array Computations](#an-embedded-language-for-accelerated-array-computations)\n  - [A simple example](#a-simple-example)\n  - [Availability](#availability)\n  - [Additional components](#additional-components)\n  - [Requirements](#requirements)\n  - [Documentation](#documentation)\n  - [Examples](#examples)\n  - [Who are we?](#who-are-we)\n  - [Mailing list and contacts](#mailing-list-and-contacts)\n  - [Citing Accelerate](#citing-accelerate)\n  - [What's missing?](#whats-missing)\n\nA simple example\n----------------\n\nAs a simple example, consider the computation of a dot product of two vectors of single-precision floating-point numbers:\n\n    dotp :: Acc (Vector Float) -\u003e Acc (Vector Float) -\u003e Acc (Scalar Float)\n    dotp xs ys = fold (+) 0 (zipWith (*) xs ys)\n\nExcept for the type, this code is almost the same as the corresponding Haskell code on lists of floats. The types indicate that the computation may be online-compiled for performance; for example, using `Data.Array.Accelerate.LLVM.PTX.run` it may be on-the-fly off-loaded to a GPU.\n\nAvailability\n------------\n\nPackage _Accelerate_ is available from:\n\n * Hackage: [accelerate][Hackage] - install with `cabal install accelerate`\n * GitHub: [AccelerateHS/accelerate][GitHub] - get the source with `git clone https://github.com/AccelerateHS/accelerate.git` \n\nTo install the Haskell toolchain try [GHCup](https://www.haskell.org/ghcup/).\n\nAdditional components\n---------------------\n\nThe following supported add-ons are available as separate packages:\n\n  * [accelerate-llvm-native][accelerate-llvm-native]: Backend targeting multicore CPUs\n  * [accelerate-llvm-ptx][accelerate-llvm-ptx]: Backend targeting CUDA-enabled NVIDIA GPUs. Requires a GPU with compute capability 2.0 or greater (see the [table on Wikipedia][wiki-cc])\n  * [accelerate-examples][accelerate-examples]: Computational kernels and applications showcasing the use of Accelerate as well as a regression test suite (supporting function and performance testing)\n  * Conversion between various formats:\n    * [accelerate-io](https://hackage.haskell.org/package/accelerate-io): For copying data directly between raw pointers\n    * [accelerate-io-array](https://hackage.haskell.org/package/accelerate-io-array): Immutable arrays\n    * [accelerate-io-bmp](https://hackage.haskell.org/package/accelerate-io-bmp): Uncompressed BMP image files\n    * [accelerate-io-bytestring](https://hackage.haskell.org/package/accelerate-io-bytestring): Compact, immutable binary data\n    * [accelerate-io-cereal](https://hackage.haskell.org/package/accelerate-io-cereal): Binary serialisation of arrays using [cereal](https://hackage.haskell.org/package/cereal)\n    * [accelerate-io-JuicyPixels](https://hackage.haskell.org/package/accelerate-io-JuicyPixels): Images in various pixel formats\n    * [accelerate-io-repa](https://hackage.haskell.org/package/accelerate-io-repa): Another Haskell library for high-performance parallel arrays\n    * [accelerate-io-serialise](https://hackage.haskell.org/package/accelerate-io-serialise): Binary serialisation of arrays using [serialise](https://hackage.haskell.org/package/serialise)\n    * [accelerate-io-vector](https://hackage.haskell.org/package/accelerate-io-vector): Efficient boxed and unboxed one-dimensional arrays\n  * [accelerate-fft][accelerate-fft]: Fast Fourier transform implementation, with FFI bindings to optimised implementations\n  * [accelerate-blas][accelerate-blas]: BLAS and LAPACK operations, with FFI bindings to optimised implementations\n  * [accelerate-bignum][accelerate-bignum]: Fixed-width large integer arithmetic\n  * [colour-accelerate][colour-accelerate]: Colour representations in Accelerate (RGB, sRGB, HSV, and HSL)\n  * [containers-accelerate](http://hackage.haskell.org/package/containers-accelerate): Hashing-based container types\n  * [gloss-accelerate][gloss-accelerate]: Generate [gloss][gloss] pictures from Accelerate\n  * [gloss-raster-accelerate][gloss-raster-accelerate]: Parallel rendering of raster images and animations\n  * [hashable-accelerate](http://hackage.haskell.org/package/hashable-accelerate): A class for types which can be converted into a hash value\n  * [lens-accelerate][lens-accelerate]: [Lens][lens] operators for Accelerate types\n  * [linear-accelerate][linear-accelerate]: [Linear][linear] vector spaces in Accelerate\n  * [mwc-random-accelerate][mwc-random-accelerate]: Generate Accelerate arrays filled with high quality pseudorandom numbers\n  * [numeric-prelude-accelerate][numeric-prelude-accelerate]: Lifting the [numeric-prelude][numeric-prelude] to Accelerate\n  * [wigner-ville-accelerate](https://github.com/Haskell-mouse/wigner-ville-accelerate): Wigner-Ville time-frequency distribution.\n\nInstall them from Hackage with `cabal install PACKAGENAME`.\n\n\nDocumentation\n-------------\n\n  * Haddock documentation is included and linked with the individual package releases on [Hackage][Hackage].\n  \u003c!-- * Haddock documentation for in-development components can be found [here](http://tmcdonell-bot.github.io/accelerate-travis-buildbot/). --\u003e\n  * The idea behind the HOAS (higher-order abstract syntax) to de-Bruijn conversion used in the library is [described separately][HOAS-conv].\n\nExamples\n--------\n\n### accelerate-examples\n\nThe [accelerate-examples][accelerate-examples] package provides a range of computational kernels and a few complete applications. To install these from Hackage, issue `cabal install accelerate-examples`. The examples include:\n\n  * An implementation of [canny edge detection][wiki-canny]\n  * An interactive [mandelbrot set][wiki-mandelbrot] generator\n  * An [N-body simulation][wiki-nbody] of gravitational attraction between solid particles\n  * An implementation of the [PageRank][wiki-pagerank] algorithm\n  * A simple [ray-tracer][wiki-raytracing]\n  * A particle based simulation of stable fluid flows\n  * A cellular automata simulation\n  * A \"password recovery\" tool, for dictionary lookup of MD5 hashes\n\n[![Mandelbrot](https://i.imgur.com/5Tbsp1j.jpg \"accelerate-mandelbrot\")](https://i.imgur.com/RgXRqsc.jpg)\n[![Raytracer](https://i.imgur.com/7ohhKm9.jpg \"accelerate-ray\")](https://i.imgur.com/ZNEGEJK.jpg)\n\n\u003c!--\n\u003cvideo width=400 height=300 controls=false autoplay loop\u003e\n  \u003csource=\"http://www.cse.unsw.edu.au/~tmcdonell/images/ray.mp4\" type=\"video/mp4\"\u003e\n\u003c/video\u003e\n--\u003e\n\n\n### LULESH\n\n[LULESH-accelerate][lulesh-accelerate] is in implementation of the Livermore Unstructured Lagrangian Explicit Shock Hydrodynamics (LULESH) mini-app. [LULESH][LULESH] represents a typical hydrodynamics code such as [ALE3D][ALE3D], but is a highly simplified application, hard-coded to solve the Sedov blast problem on an unstructured hexahedron mesh.\n\n![LULESH mesh](https://i.imgur.com/bIkODKd.jpg)\n\n\n### Additional examples\n\nAccelerate users have also built some substantial applications of their own.\nPlease feel free to add your own examples!\n\n  * Jonathan Fraser, [GPUVAC](https://github.com/GeneralFusion/gpuvac): An explicit advection magnetohydrodynamics simulation\n  * David van Balen, [Sudokus](https://github.com/dpvanbalen/Sudokus): A sudoku solver\n  * Trevor L. McDonell, [lol-accelerate][lol-accelerate]: A backend to the Λ ○ λ ([Lol][lol]) library for ring-based lattice cryptography\n  * Henning Thielemann, [patch-image](http://hackage.haskell.org/package/patch-image): Combine a collage of overlapping images\n  * apunktbau, [bildpunkt](https://github.com/abau/bildpunkt): A ray-marching distance field renderer\n  * klarh, [hasdy](https://github.com/klarh/hasdy): Molecular dynamics in Haskell using Accelerate\n  * Alexandros Gremm used Accelerate as part of the [2014 CSCS summer school](http://user.cscs.ch/blog/2014/cscs_usi_summer_school_2014_30_june_10_july_2014_in_serpiano_tessin/index.html) ([code](https://github.com/agremm/cscs))\n\n\nWho are we?\n-----------\n\nThe Accelerate team (past and present) consists of:\n\n  * Manuel M T Chakravarty ([@mchakravarty])  \u003c!-- 2008..2017? --\u003e\n  * Gabriele Keller ([@gckeller])             \u003c!-- 2008..     --\u003e\n  * Trevor L. McDonell ([@tmcdonell])         \u003c!-- 2009..     --\u003e\n  * Robert Clifton-Everest ([@robeverest])    \u003c!-- 2013..     --\u003e\n  * Frederik M. Madsen ([@fmma])              \u003c!-- 2014       --\u003e\n  * Ryan R. Newton ([@rrnewton])              \u003c!-- 2012..2013 --\u003e\n  * Joshua Meredith ([@JoshMeredith])         \u003c!-- 2018..     --\u003e\n  * Ben Lever ([@blever])                     \u003c!-- 2010..2011 --\u003e\n  * Sean Seefried ([@sseefried])              \u003c!-- 2010..2011 --\u003e\n  * Ivo Gabe de Wolff ([@ivogabe])            \u003c!-- 2019..     --\u003e\n\nThe maintainer and principal developer of Accelerate is Trevor L.\nMcDonell \u003ctrevor.mcdonell@gmail.com\u003e.\n\n\nMailing list and contacts\n-------------------------\n\n  * Mailing list: [`accelerate-haskell@googlegroups.com`](mailto:accelerate-haskell@googlegroups.com) (discussions on both use and development are welcome)\n  * Sign up for the mailing list at the [Accelerate Google Groups page][Google-Group]\n  * Bug reports and issues tracking: [GitHub project page][Issues]\n  * Chat with us on [gitter](https://gitter.im/AccelerateHS/Lobby)\n\n\nCiting Accelerate\n-----------------\n\nIf you use Accelerate for academic research, you are encouraged (though not\nrequired) to cite the following papers:\n\u003c!-- ([BibTeX](http://www.cse.unsw.edu.au/~tmcdonell/papers/accelerate.bib)): --\u003e\n\n  * Manuel M. T. Chakravarty, Gabriele Keller, Sean Lee, Trevor L. McDonell, and Vinod Grover.\n    [Accelerating Haskell Array Codes with Multicore GPUs][CKLM+11].\n    In _DAMP '11: Declarative Aspects of Multicore Programming_, ACM, 2011.\n\n  * Trevor L. McDonell, Manuel M. T. Chakravarty, Gabriele Keller, and Ben Lippmeier.\n    [Optimising Purely Functional GPU Programs][MCKL13].\n    In _ICFP '13: The 18th ACM SIGPLAN International Conference on Functional Programming_, ACM, 2013.\n\n  * Robert Clifton-Everest, Trevor L. McDonell, Manuel M. T. Chakravarty, and Gabriele Keller.\n    [Embedding Foreign Code][CMCK14].\n    In _PADL '14: The 16th International Symposium on Practical Aspects of Declarative Languages_, Springer-Verlag, LNCS, 2014.\n\n  * Trevor L. McDonell, Manuel M. T. Chakravarty, Vinod Grover, and Ryan R. Newton.\n    [Type-safe Runtime Code Generation: Accelerate to LLVM][MCGN15].\n    In _Haskell '15: The 8th ACM SIGPLAN Symposium on Haskell_, ACM, 2015.\n\n  * Robert Clifton-Everest, Trevor L. McDonell, Manuel M. T. Chakravarty, and Gabriele Keller.\n    [Streaming Irregular Arrays][CMCK17].\n    In Haskell '17: The 10th ACM SIGPLAN Symposium on Haskell, ACM, 2017.\n\n\nAccelerate is primarily developed by academics, so citations matter a lot to us.\nAs an added benefit, you increase Accelerate's exposure and potential user (and\ndeveloper!) base, which is a benefit to all users of Accelerate. Thanks in advance!\n\n\nWhat's missing?\n---------------\n\nHere is a list of features that are currently missing:\n\n * Preliminary API (parts of the API may still change in subsequent releases)\n * Many more features... contact us!\n\n  [@mchakravarty]:              https://github.com/mchakravarty\n  [@gckeller]:                  https://github.com/gckeller\n  [@tmcdonell]:                 https://github.com/tmcdonell\n  [@robeverest]:                https://github.com/robeverest\n  [@fmma]:                      https://github.com/fmma\n  [@rrnewton]:                  https://github.com/rrnewton\n  [@JoshMeredith]:              https://github.com/JoshMeredith\n  [@blever]:                    https://github.com/blever\n  [@sseefried]:                 https://github.com/sseefried\n  [@ivogabe]:                   https://github.com/ivogabe\n\n  [CKLM+11]:                    https://github.com/tmcdonell/tmcdonell.github.io/raw/master/papers/acc-cuda-damp2011.pdf\n  [MCKL13]:                     https://github.com/tmcdonell/tmcdonell.github.io/raw/master/papers/acc-optim-icfp2013.pdf\n  [MCKL13-slides]:              https://speakerdeck.com/tmcdonell/optimising-purely-functional-gpu-programs\n  [CMCK14]:                     https://github.com/tmcdonell/tmcdonell.github.io/raw/master/papers/acc-ffi-padl2014.pdf\n  [MCGN15]:                     https://github.com/tmcdonell/tmcdonell.github.io/raw/master/papers/acc-llvm-haskell2015.pdf\n  [MCGN15-slides]:              https://speakerdeck.com/tmcdonell/type-safe-runtime-code-generation-accelerate-to-llvm\n  [MCGN15-video]:               https://www.youtube.com/watch?v=snXhXA5noVc\n  [HIW'09]:                     https://wiki.haskell.org/HaskellImplementorsWorkshop\n  [CMCK17]:                     https://github.com/tmcdonell/tmcdonell.github.io/raw/master/papers/acc-seq2-haskell2017.pdf\n  [CMCK17-video]:               https://www.youtube.com/watch?v=QIWSqp7AaNo\n  [Mar13]:                      http://chimera.labs.oreilly.com/books/1230000000929\n  [Embedded]:                   https://speakerdeck.com/mchakravarty/embedded-languages-for-high-performance-computing-in-haskell\n  [Hackage]:                    http://hackage.haskell.org/package/accelerate\n  [accelerate-cuda]:            https://github.com/AccelerateHS/accelerate-cuda\n  [accelerate-examples]:        https://github.com/AccelerateHS/accelerate-examples\n  [accelerate-io]:              https://github.com/AccelerateHS/accelerate-io\n  [accelerate-fft]:             https://github.com/AccelerateHS/accelerate-fft\n  [accelerate-blas]:            https://github.com/tmcdonell/accelerate-blas\n  [accelerate-backend-kit]:     https://github.com/AccelerateHS/accelerate-backend-kit\n  [accelerate-buildbot]:        https://github.com/AccelerateHS/accelerate-buildbot\n  [accelerate-repa]:            https://github.com/blambo/accelerate-repa\n  [accelerate-opencl]:          https://github.com/hiPERFIT/accelerate-opencl\n  [accelerate-cabal]:           https://github.com/AccelerateHS/accelerate/accelerate.cabal\n  [accelerate-cuda-cabal]:      https://github.com/AccelerateHS/accelerate-cuda/accelerate-cuda.cabal\n  [accelerate-llvm]:            https://github.com/AccelerateHS/accelerate-llvm\n  [accelerate-llvm-native]:     https://github.com/AccelerateHS/accelerate-llvm\n  [accelerate-llvm-ptx]:        https://github.com/AccelerateHS/accelerate-llvm\n  [accelerate-bignum]:          https://github.com/tmcdonell/accelerate-bignum\n  [GitHub]:                     https://github.com/AccelerateHS/accelerate\n  [Wiki]:                       https://github.com/AccelerateHS/accelerate/wiki\n  [Issues]:                     https://github.com/AccelerateHS/accelerate/issues\n  [Google-Group]:               http://groups.google.com/group/accelerate-haskell\n  [HOAS-conv]:                  https://github.com/mchakravarty/hoas-conv\n  \u003c!-- [HOAS-conv]:                  https://web.archive.org/web/20180805092417/http://www.cse.unsw.edu.au/~chak/haskell/term-conv/ --\u003e\n  [repa]:                       http://hackage.haskell.org/package/repa\n  [wiki-cc]:                    https://en.wikipedia.org/wiki/CUDA#Supported_GPUs\n  [YLJ13-video]:                http://youtu.be/ARqE4yT2Z0o\n  [YLJ13-slides]:               https://speakerdeck.com/tmcdonell/gpgpu-programming-in-haskell-with-accelerate\n  [YLJ13-workshop]:             https://speakerdeck.com/tmcdonell/gpgpu-programming-in-haskell-with-accelerate-workshop\n  [wiki-canny]:                 https://en.wikipedia.org/wiki/Canny_edge_detector\n  [wiki-mandelbrot]:            https://en.wikipedia.org/wiki/Mandelbrot_set\n  [wiki-nbody]:                 https://en.wikipedia.org/wiki/N-body\n  [wiki-raytracing]:            https://en.wikipedia.org/wiki/Ray_tracing\n  [wiki-pagerank]:              https://en.wikipedia.org/wiki/Pagerank\n  [Trevor-thesis]:              https://github.com/tmcdonell/tmcdonell.github.io/raw/master/papers/TrevorMcDonell_PhD_Thesis.pdf\n  [colour-accelerate]:          https://github.com/tmcdonell/colour-accelerate\n  [gloss]:                      https://hackage.haskell.org/package/gloss\n  [gloss-accelerate]:           https://github.com/tmcdonell/gloss-accelerate\n  [gloss-raster-accelerate]:    https://github.com/tmcdonell/gloss-raster-accelerate\n  [lens]:                       https://hackage.haskell.org/package/lens\n  [lens-accelerate]:            https://github.com/tmcdonell/lens-accelerate\n  [linear]:                     https://hackage.haskell.org/package/linear\n  [linear-accelerate]:          https://github.com/tmcdonell/linear-accelerate\n  [mwc-random-accelerate]:      https://github.com/tmcdonell/mwc-random-accelerate\n  [numeric-prelude]:            https://hackage.haskell.org/package/numeric-prelude\n  [numeric-prelude-accelerate]: https://github.com/tmcdonell/numeric-prelude-accelerate\n  [LULESH]:                     https://codesign.llnl.gov/lulesh.php\n  [ALE3D]:                      https://wci.llnl.gov/simulation/computer-codes/ale3d\n  [lulesh-accelerate]:          https://github.com/tmcdonell/lulesh-accelerate\n  [lol]:                        https://hackage.haskell.org/package/lol\n  [lol-accelerate]:             https://github.com/tmcdonell/lol-accelerate\n\n","funding_links":[],"categories":["Haskell","Functional"],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FAccelerateHS%2Faccelerate","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FAccelerateHS%2Faccelerate","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FAccelerateHS%2Faccelerate/lists"}