Thanks a lot, I would be curious to check the performance of the latest MKL by Intel, especially since I am dealing with larger matrices than the ones used in the test. ATLAS would be an idea, but according to Intel ATLAS and MKL have pretty much the same performance (for single-threaded execution), and I would not know how to link its libraries to the building environment in vxl.

About MKL, I patched the CMakeLists for vnl, but the FindMKL module is included neither in the 2.4 nor in the 2.6 release of Cmake. I also looked for it in the vxl repository but I could not find it - does anyone have any pointers?

Regards,
Marco

On Tue, Jul 22, 2008 at 5:57 PM, Ian Scott <ian.m.scott@student.manchester.ac.uk> wrote:
I had a go once many years ago - using Intel's BLAS (branded as the Maths Kernel Library.)

A patch to the CMakeLists.txt and some results can be found in the mailing list archive at
https://sourceforge.net/mailarchive/message.php?msg_name=000801c2c3ce%2473299720%2422ea5882%40smb.man.ac.uk

Surprisingly (until I looked into it some more), when gcc compiled our own fairly naive vector_vector and vector_matrix operations (Level's 1 and 2 of BLAS), they were significantly faster than Intel's Library, on the medium size scale of problems I was interested in.

If you are doing large LAPACK or vnl_algo operations in a time constrained section of code, then it almost certainly is worth using something like ATLAS or MKL.

Regards,
Ian.

 Marco Visentini Scarzanella wrote:
Hi, from what I can gather VXL comes with its own implementation of BLAS and LAPACK. Is it possible to make it use some optimized machine-targeted libraries such as ATLAS?

Regards,

Marco


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