There were several reasons we initially decided to include MKL, an extensively threaded, highly optimized library, in the Enthought Python Distribution. For one thing, we like that MKL detects the processing capability of the machine and then runs optimal algorithm for that hardware. Secondly, we knew that MKL would offer faster linear algebra routines than the ATLAS framework, previously used for EPD Linux and Windows, and Accelerate library, previously used for OSX.
We didn’t anticipate, however, just how dramatic that speed up would be. Our benchmarking tests document the astounding increases in processing speed that MKL lends to EPD.
In EPD 6.1, NumPy and SciPy are dynamically linked against the MKL linear algebra routines. This allows EPD users to seamlessly benefit from the highly optimized BLAS and LAPACK routines in the MKL. In addition, EPD 6.1 comes bundled with all of the MKL run-time libraries so that advanced users can take advantage (with ctypes) of even more of the MKL library such as fast Fourier transforms, trust-region optimization methods, sparse solvers, and vector math.
We’re really pleased with the optimizations MKL offers to our EPD users. Try out EPD 6.1 for yourself!