Software:Parallel Colt: Difference between revisions

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DoubleMatrix2D result = alg.mult(matA,matB);
DoubleMatrix2D result = alg.mult(matA,matB);
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==See also==
* [[Software:List of free and open-source software packages#Mathematical libraries|List of open-source mathematical libraries]]


== References ==
== References ==
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<references>
 
<ref name=ProjectPage>{{cite web |url=https://sites.google.com/site/piotrwendykier/software/parallelcolt Official site |title=Parallel Colt Project Page |work=Parallel Colt|accessdate=June 15, 2013}}</ref>
<ref name=ProjectPage>{{cite web |url=https://sites.google.com/site/piotrwendykier/software/parallelcolt Official site |title=Parallel Colt Project Page |work=Parallel Colt|accessdate=June 15, 2013}}</ref>
}}
 
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[[Category:Java (programming language) libraries]]
[[Category:Java (programming language) libraries]]


{{Sourceattribution|Parallel Colt}}
{{Sourceattribution|Parallel Colt}}

Latest revision as of 06:54, 12 April 2026

Parallel Colt
Original author(s)Piotr Wendykier
Stable release
0.9.4 / March 21, 2010 (2010-03-21)
Operating systemCross-platform
TypeLibrary
LicenseVarious
Websitesites.google.com/site/piotrwendykier/software/parallelcolt

Parallel Colt is a set of multithreaded version of Colt. It is a collection of open-source libraries for High Performance Scientific and Technical Computing written in Java. It contains all the original capabilities of Colt and adds several new ones, with a focus on multi-threaded algorithms.

Capabilities

Parallel Colt has all the capabilities of the original Colt library, with the following additions.[1]

  • Multithreading
  • Specialized Matrix data structures
  • JPlasma
    • Java port of PLASMA (Parallel Linear Algebra for Scalable Multi-core Architectures).
  • CSparseJ
    • CSparseJ is a Java port of CSparse (a Concise Sparse matrix package).
  • Netlib-java
    • Netlib is a collection of mission-critical software components for linear algebra systems (i.e. working with vectors or matrices).
  • Solvers and preconditioners
  • Nonlinear Optimization
    • Java translations of the 1-dimensional minimization routine from the MINPACK
  • Matrix reader/writer
  • All classes that use floating-point arithmetic are implemented in single and double precision.
  • Parallel quicksort algorithm

Usage example

Example of singular value decomposition (SVD):

DenseDoubleAlgebra alg = new DenseDoubleAlgebra();
DenseDoubleSingularValueDecomposition s = alg.svd(matA);

DoubleMatrix2D U = s.getU();
DoubleMatrix2D S = s.getS();
DoubleMatrix2D V = s.getV();

Example of matrix multiplication:

DenseDoubleAlgebra alg = new DenseDoubleAlgebra();
DoubleMatrix2D result = alg.mult(matA,matB);

See also

References