= Repa (REgular PArallel arrays) = Repa provides high performance, regular, multi-dimensional, shape polymorphic parallel arrays. All numeric data is stored unboxed. Functions written with the Repa combinators are automatically parallel provided you supply +RTS -Nwhatever on the command line when running the program. Repa means "turnip" in Russian. If you don't like turnips then this library probably isn't for you. == Download == Repa is split up into a few packages to help control dependencies. || [http://hackage.haskell.org/package/repa repa] || The base library, defining the array type and combinators. || || [http://hackage.haskell.org/package/repa-io repa-io] || Reading and writing arrays in various formats, including BMP. || || [http://hackage.haskell.org/package/repa-algorithms repa-algorithms] || Some reusable matrix algorithms. || || [http://hackage.haskell.org/package/repa-devil repa-devil] || Bindings to the DevIL image library. || || [http://hackage.haskell.org/package/repa-examples repa-examples] || Example applications. || If you want the lot then just install the examples, and this will pull down the rest. {{{ cabal install repa-examples }}} == From the source repo == The darcs source repos are at [http://code.ouroborus.net/repa/] == Frequently Asked Questions (FAQ) == Q: GHC complains: `ghc.exe: could not execute opt` what's up with that? [[br]] A: `opt` is the LLVM optimiser. You need to install [http://llvm.org/ LLVM]. Q: Does Repa's implicit parallelism extend to distributed memory machines? [[br]] A: No. Repa supports shared memory / multi-core parallelism only. Repa's approach to array fusion could be used in a distributed setting, but managing the extra communication is a significant amount of extra work. We have no current plans to target distributed machines. Q: How to I map a function across all slices in an array? [[br]] A: This is not possible in general because we can't guarantee that the worker function will return a slice with the same shape as the original. If the slices only contain a few elements (up to six) then your best bet is to use an array of tuples and use the regular `map` function to apply the worker to all the tuples. This approach is limited to 6-tuples because that's the maximum the `Data.Vector` library currently supports, and `Repa` uses `Data.Vector` to store manifest arrays. See #22 for discussion. Q: Why is my program so slow? [[br]] A: Probably because you haven't forced your source arrays before traversing them, or haven't added enough INLINE pragmas. In particular you may need to force image arrays read with the BMP routines before traversing them. Read [http://www.haskell.org/haskellwiki/Numeric_Haskell:_A_Repa_Tutorial#Optimising_Repa_programs the optimisation section] in the tutorial, then [http://stackoverflow.com/questions/6300428/poor-performance-with-transpose-and-cumulative-sum-in-repa/6340867#6340867 This Answer] on Stack Overflow. If it's still a problem then continue on... Q: Where do I ask further questions? [[br]] A: Ask on [http://stackoverflow.com/search?q=repa Stack Overflow], then send a link to `repa@ouroborus.net` if that doesn't help. == Report a bug == To report bugs, request features, or get an account on the trac, please send email to `repa@ouroborus.net` == Papers and Tutorials == [http://www.cse.unsw.edu.au/~benl/papers/repa/repa-icfp2010.pdf Regular Shape Polymorphic Arrays in Haskell] * Describes the overall approach to fusion, and how the shape polymorphism works. * Since this paper was published, the internals have changed slightly, but the overall structure is the same. [http://www.cse.unsw.edu.au/~benl/papers/stencil/stencil-haskell2011-sub.pdf Efficient Parallel Stencil Convolution in Haskell] * Describes the current array representation. * Discusses the back-end, how the parallelism works, and how to write fast code. * Describes Repa's special support for Stencil convolutions. [http://www.haskell.org/haskellwiki/Numeric_Haskell:_A_Repa_Tutorial Tutorial on usage and optimisation] * Contains lots of simple examples to get you started. * High level discussion of fusion, optimisation, and how to use the {{{force}}} function. == Examples == Here is the output of some of the examples included in the [http://hackage.haskell.org/package/repa-examples repa-examples] package: || fft2d-highpass || Laplace || Crystal || || [[Image(WikiStart:lena-high2-thumb.jpg)]] || [[Image(WikiStart:pls-400x400-out-thumb.jpg)]] || [[Image(WikiStart:crystal-thumb.png)]] || || [wiki:Examples/Fft2dHighpass more info] || [wiki:Examples/Laplace more info] || [http://code.ouroborus.net/gloss/gloss-stable/gloss-examples/raster/Crystal/Main.hs source] [http://www.youtube.com/watch?v=v_0Yyl19fiI video] || == Demo == There is also an OSX demo that does edge detection on a video stream: || [[Image(WikiStart:beholder-thumb.jpg)]] || || [http://code.ouroborus.net/beholder/beholder-head/ source] [http://www.youtube.com/watch?v=UGN0GxGEDsY video] || * The [http://code.ouroborus.net/beholder/beholder-head/ source] should compile with XCode 3.2.1, GHC 7.0.3 and Repa 2.0.0, but you need to update and run the CONFIGURE.sh script to point it to your GHC install. * There are also prebuilt OSX i386 versions for [http://code.ouroborus.net/beholder/distro/beholder-N2.tgz two] [http://code.ouroborus.net/beholder/distro/beholder-N4.tgz four] and [http://code.ouroborus.net/beholder/distro/beholder-N6.tgz six] threads. These just have the corresponding +RTS -N# option baked in, you can set it in the main.m module. Some day I will make it so you can select this from the GUI. * You can also run the edge detector over a single uncompressed .bmp file using the repa-canny program from the [http://hackage.haskell.org/package/repa-examples repa-examples] package.