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Hello,

>To summarize are you aware of any benchmark of sequential VS parallel VML and multi dimensional FFT?

This paper includes the performance on MKL DFT functions:

http://software.intel.com/sites/products/collateral/hpc/mkl/mkl_indepth.pdf

For VML performance, The actual performance depends on a number of factors.You may check MKL manual, VML part document:

The actual performance depends on a number of factors, including vectorization and threading overhead. The recommended usage tips are as follows:

>On vector lengths less than 10-50, use math functions provided by Intel compilers rather than the VML functions

>On vector lengths between 10-50 to 1000-5000, use sequential VML

>On vector lengths larger than 1000-5000, use threaded VML.

Thanks,

Chao

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In the accelerator device field (CUDA, MIC, ....) more people are betting against OpenMP than with it, but there are working OpenMP and OpenMP-like implementations.

You haven't said enough about similar to what for anyone to risk judgment. OpenMP does an excellent job on many scientific applications, and is likely to continue getting support across a wider variety of platforms.

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Hello,

>To summarize are you aware of any benchmark of sequential VS parallel VML and multi dimensional FFT?

This paper includes the performance on MKL DFT functions:

http://software.intel.com/sites/products/collateral/hpc/mkl/mkl_indepth.pdf

For VML performance, The actual performance depends on a number of factors.You may check MKL manual, VML part document:

The actual performance depends on a number of factors, including vectorization and threading overhead. The recommended usage tips are as follows:

>On vector lengths less than 10-50, use math functions provided by Intel compilers rather than the VML functions

>On vector lengths between 10-50 to 1000-5000, use sequential VML

>On vector lengths larger than 1000-5000, use threaded VML.

Thanks,

Chao

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