Project details for jblas

Screenshot jblas 0.2

by mikio - May 8, 2009, 12:34:39 CET [ Project Homepage BibTeX Download ]

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jblas is a fast linear algebra library for Java. jblas is based on BLAS and LAPACK, the de-facto industry standard for matrix computations, and uses state-of-the-art implementations like ATLAS for all its computational routines, making jBLAS very fast.

jblas can is essentially a light-wight wrapper around the BLAS and LAPACK routines. These packages have originated in the Fortran community which explains their archaic API. On the other hand modern implementations are hard to beat performance wise. jblas aims to make this functionality available to Java programmers such that they do not have to worry about writing JNI interfaces and calling conventions of Fortran code.

Changes to previous version:

Release v2.0 - May 8, 2009

Release v2.0 mostly adds features to the build process and renames some packages and classes to make the structure simpler. No new significant functionality has been added.

Changes from version v1.0:

  • fixed a bug in DoubleMatrix.copy()
  • Renamed packages to make structure simpler -> org.jblas -> org.jblas.exceptions -> org.jblas.ranges org.jblas.core -> moved content to org.jblas and org.jblas.util

  • Renamed classes

Blas -> NativeBlas

  • Build process can now generate different kinds of jar files, and also generate shared libraries which are statically linked against BLAS, LAPACK or ATLAS. You can also generate a multiplatform jar file which contains shared libraries for different platforms.

  • Generated wrapper code has been optimized a bit:

  • in the lapack wrapper with automatic workspace allocation, only small dummy arrays are passed in the workspace query, meaning that the real arrays are only passed once, not twice.

  • The wrapper now also parses information whether output variables are input or output and releases the arrays with JNI_ABORT in case they are not output variables. This should also reduce the amount of copying.

BibTeX Entry: Download
Supported Operating Systems: Cygwin, Linux, Windows
Data Formats: Ascii
Tags: Linear Algebra, Matrix
Archive: download here


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