ADPfusion: Efficient, high-level dynamic programming.
|Versions||0.0.1.0, 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, 0.5.0.0, 0.5.1.0, 0.5.2.0, 0.5.2.2 (info)|
|Dependencies||base (==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]|
|Copyright||Christian Hoener zu Siederdissen, 2011-2013|
|Author||Christian Hoener zu Siederdissen, 2011-2013|
|Category||Algorithms, Data Structures, Bioinformatics|
|Source repo||head: git clone git://github.com/choener/ADPfusion|
|Uploaded||by ChristianHoener at Fri Dec 6 21:39:38 UTC 2013|
|Downloads||8041 total (109 in the last 30 days)|
|Rating||(no votes yet) [estimated by rule of succession]|
|Status||Docs not available [build log]
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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
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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