Performance of Java matrix math libraries?
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:
I searched and found nothing.
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
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.