Performance of Java matrix math libraries? [closed

Question

We are computing something whose runtime is bound by matrix operations. (Some details below if interested.) This experience prompted the following question:

Do folk have experience with the performance of Java libraries for matrix math (e.g., multiply, inverse, etc.)? For example:

• JAMA
• COLT
• Apache commons math

I searched and found nothing.

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Details of our speed comparison:

We are using Intel FORTRAN (ifort (IFORT) 10.1 20070913). We have reimplemented it in Java (1.6) using Apache commons math 1.2 matrix ops, and it agrees to all of its digits of accuracy. (We have reasons for wanting it in Java.) (Java doubles, Fortran real*8). Fortran: 6 minutes, Java 33 minutes, same machine. jvisualm profiling shows much time spent in RealMatrixImpl.{getEntry,isValidCoordinate} (which appear to be gone in unreleased Apache commons math 2.0, but 2.0 is no faster). Fortran is using Atlas BLAS routines (dpotrf, etc.).

Obviously this could depend on our code in each language, but we believe most of the time is in equivalent matrix operations.

In several other computations that do not involve libraries, Java has not been much slower, and sometimes much faster.

Answer 1

Linalg code that relies heavily on Pentiums and later processors' vector computing capabilities (starting with the MMX extensions, like LAPACK and now Atlas BLAS) is not "fantastically optimized", but simply industry-standard. To replicate that perfomance in Java you are going to need native libraries. I have had the same performance problem as you describe (mainly, to be able to compute Choleski decompositions) and have found nothing really efficient: Jama is pure Java, since it is supposed to be just a template and reference kit for implementers to follow... which never happened. You know Apache math commons... As for COLT, I have still to test it but it seems to rely heavily on Ninja improvements, most of which were reached by building an ad-hoc Java compiler, so I doubt it's going to help. At that point, I think we "just" need a collective effort to build a native Jama implementation...

Answer 2

You may want to check out the jblas project. It's a relatively new Java library that uses BLAS, LAPACK and ATLAS for high-performance matrix operations.

The developer has posted some benchmarks in which jblas comes off favourably against MTJ and Colt.

Answer 3

We have used COLT for some pretty large serious financial calculations and have been very happy with it. In our heavily profiled code we have almost never had to replace a COLT implementation with one of our own.

In their own testing (obviously not independent) I think they claim within a factor of 2 of the Intel hand-optimised assembler routines. The trick to using it well is making sure that you understand their design philosophy, and avoid extraneous object allocation.

Answer 4

Jeigen https://github.com/hughperkins/jeigen

• wraps Eigen C++ library http://eigen.tuxfamily.org , which is one of the fastest free C++ libraries available
• relatively terse syntax, eg 'mmul', 'sub'
• handles both dense and sparse matrices

A quick test, by multiplying two dense matrices, ie:

import static jeigen.MatrixUtil.*;

int K = 100;
int N = 100000;
DenseMatrix A = rand(N, K);
DenseMatrix B = rand(K, N);
Timer timer = new Timer();
DenseMatrix C = B.mmul(A);
timer.printTimeCheckMilliseconds();

Results:

Jama: 4090 ms
Jblas: 1594 ms
Ojalgo: 2381 ms (using two threads)
Jeigen: 2514 ms

• Compared to jama, everything is faster :-P
• Compared to jblas, Jeigen is not quite as fast, but it handles sparse matrices.
• Compared to ojalgo, Jeigen takes about the same amount of elapsed time, but only using one core, so Jeigen uses half the total cpu. Jeigen has a terser syntax, ie 'mmul' versus 'multiplyRight'

Answer 5

I'm the author of Java Matrix Benchmark (JMatBench) and I'll give my thoughts on this discussion.

There are significant difference between Java libraries and while there is no clear winner across the whole range of operations, there are a few clear leaders as can be seen in the latest performance results (October 2013).

If you are working with "large" matrices and can use native libraries, then the clear winner (about 3.5x faster) is MTJ with system optimised netlib. If you need a pure Java solution then MTJ, OjAlgo, EJML and Parallel Colt are good choices. For small matrices EJML is the clear winner.

The libraries I did not mention showed significant performance issues or were missing key features.

Answer 6

There's also UJMP