Zuling Kang shares with us how we can use native math libraries in netlib-java to speed up certain machine learning algorithms in Apache Spark:
Spark’s MLlib uses the Breeze linear algebra package, which depends on netlib-java for optimized numerical processing. netlib-java is a wrapper for low-level BLAS, LAPACK, and ARPACK libraries. However, due to licensing issues with runtime proprietary binaries, neither the Cloudera distribution of Spark nor the community version of Apache Spark includes the netlib-java native proxies by default. So without manual configuration, netlib-java only uses the F2J library, a Java-based math library that is translated from Fortran77 reference source code.
To check whether you are using native math libraries in Spark ML or the Java-based F2J, use the Spark shell to load and print the implementation library of netlib-java. The following commands return information on the BLAS library and include that it is using F2J in the line, “com.github.fommil.netlib.F2jBLAS,” which is highlighted below:
In the examples here, you can get about a 2x difference using the native math libraries versus without, so although that’s not an order of magnitude difference, it’s still nothing to sneeze at.