The ADPfusion package

[Tags: bsd3, library]

ADPfusion combines stream-fusion (using the stream interface provided by the vector library) and type-level programming to provide highly efficient dynamic programming combinators.

From the programmers' viewpoint, ADPfusion behaves very much like the original ADP implementation http://bibiserv.techfak.uni-bielefeld.de/adp/ developed by Robert Giegerich and colleagues, though both combinator semantics and backtracking are different.

The library internals, however, are designed not only to speed up ADP by a large margin (which this library does), but also to provide further runtime improvements by allowing the programmer to switch over to other kinds of data structures with better time and space behaviour. Most importantly, dynamic programming tables can be strict, removing indirections present in lazy, boxed tables.

As a simple benchmark, consider the Nussinov78 algorithm which translates to three nested for loops (for C). In the figure, four different approaches are compared using inputs with size 100 characters to 1000 characters in increments of 100 characters. C is an implementation (.C directory) in C using gcc -O3. ADP is the original ADP approach (see link above), while GAPC uses the GAP language (http://gapc.eu/).

Performance comparison figure: http://www.tbi.univie.ac.at/~choener/adpfusion/gaplike-nussinov-runtime.jpg

Please note that actual performance will depend much on table layout and data structures accessed during calculations, but in general performance is very good: close to C and better than other high-level approaches (that I know of).

Even complex ADP code tends to be completely optimized to loops that use only unboxed variables (Int# and others, indexIntArray# and others).

Completely novel (compared to ADP), is the idea of allowing efficient monadic combinators. This facilitates writing code that performs backtracking, or samples structures stochastically, among others things.

Two algorithms from the realm of computational biology are provided as examples on how to write dynamic programming algorithms using this library: http://hackage.haskell.org/package/Nussinov78 and http://hackage.haskell.org/package/RNAFold.


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Versions0.0.1.0, 0.0.1.1, 0.0.1.2, 0.1.0.0, 0.2.0.0, 0.2.0.1, 0.2.0.2, 0.2.0.3, 0.2.0.4, 0.2.1.0, 0.4.0.0, 0.4.0.1, 0.4.0.2, 0.4.1.0, 0.4.1.1 (info)
Change logchangelog
Dependenciesbase (==4.*), deepseq (>=1.3), ghc-prim, primitive (>=0.5), PrimitiveArray (==0.5.2.*), QuickCheck (>=2.5), repa (>=3.2), strict (>=0.3.2), template-haskell, transformers, vector (>=0.10) [details]
LicenseBSD3
CopyrightChristian Hoener zu Siederdissen, 2011-2013
AuthorChristian Hoener zu Siederdissen, 2011-2013
Maintainerchoener@tbi.univie.ac.at
Stabilityexperimental
CategoryAlgorithms, Data Structures, Bioinformatics
Home pagehttp://www.tbi.univie.ac.at/~choener/adpfusion
Source repositoryhead: git clone git://github.com/choener/ADPfusion
ExecutablesNeedlemanWunsch
UploadedFri Dec 6 21:39:38 UTC 2013 by ChristianHoener
Downloads3167 total (174 in last 30 days)
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StatusDocs not available [build log]
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Readme for ADPfusion-0.2.0.2

ADPfusion (c) 2012, Christian Hoener zu Siederdissen University of Vienna, Vienna, Austria choener@tbi.univie.ac.at LICENSE: BSD3

Introduction

ADPfusion combines stream-fusion (using the stream interface provided by the vector library) and type-level programming to provide highly efficient dynamic programming combinators.

From the programmers' viewpoint, ADPfusion behaves very much like the original ADP implementation http://bibiserv.techfak.uni-bielefeld.de/adp/ developed by Robert Giegerich and colleagues, though both combinator semantics and backtracking are different.

The library internals, however, are designed not only to speed up ADP by a large margin (which this library does), but also to provide further runtime improvements by allowing the programmer to switch over to other kinds of data structures with better time and space behaviour. Most importantly, dynamic programming tables can be strict, removing indirections present in lazy, boxed tables.

As an example, even rather complex ADP code tends to be completely optimized to loops that use only unboxed variables (Int# and others, indexIntArray# and others).

Completely novel (compared to ADP), is the idea of allowing efficient monadic combinators. This facilitates writing code that performs backtracking, or samples structures stochastically, among others things.

This version is still highly experimental and makes use of multiple recent improvements in GHC. This is particularly true for the monadic interface.

Long term goals: Outer indices with more than two dimensions, specialized table design, a combinator library, a library for computational biology.

Two algorithms from the realm of computational biology are provided as examples on how to write dynamic programming algorithms using this library: http://hackage.haskell.org/package/Nussinov78 and http://hackage.haskell.org/package/RNAfold.

Installation

If GHC-7.2.2/GHC-7.4, LLVM and cabal-install are available, you should be all set. I recommend using cabal-dev as it provides a very nice sandbox (replace cabal-dev with cabal otherwise).

If you go with cabal-dev, no explicit installation is necessary and ADPfusion will be installed in the sandbox together with the example algorithms or your own.

For a more global installation, "cabal install ADPfusion" should do the trick.

To run the Quickcheck tests, do an additional "cabal-dev install QuickCheck", then "cabal-dev ghci", ":l ADP/Fusion/QuickCheck.hs", and "allProps". Loading the quickcheck module should take a bit due to compilation. "allProps" tests all properties and should yield no errors.

Notes

If you have problems, find bugs, or want to use this library to write your own DP algorithms, please send me a mail. I'm very interested in hearing what is missing.

One of the things I'll be integrating is an extension to higher dimensions (more than two).

Right now, I am not quite happy with the construction and destruction of the "Box" representations. These will change soon. In addition, an analysis of the actual combinators should remove the need for nested applications of objective functions in many cases.

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