https://community.intel.com/t5/Intel-oneAPI-Math-Kernel-Library/Webinar-recording-Powered-by-MKL-Accelerating-NumPy-and-SciPy/m-p/977431#M17152 <P>The webinar recording is now available online: <A href="http://software.intel.com/en-us/articles/powered-by-mkl-accelerating-numpy-and-scipy-performance-with-intel-mkl-python">http://software.intel.com/en-us/articles/powered-by-mkl-accelerating-numpy-and-scipy-performance-with-intel-mkl-python </A></P> <P>NumPy/SciPy are scientific libraries for Python. R is a programming language for statistics computing. These tools, especially their open source packages, have gained popularity among programmers in scientific and statistic computing. A fundamental component in both NumPy/SciPy and R are the linear algebra functions, which rely on the standard BLAS and LAPACK routines. Intel® Math Kernel Library (Intel® MKL) provides a high performance implementation of BLAS and LAPACK that is optimized for Intel architectures. This webinar discusses building the open source NumPy/SciPy and R with Intel MKL to significantly improve the performance of linear algebra operations. Beyond BLAS and LAPACK, there are other functions in Intel MKL that can provide great performance benefit. This webinar will give a quick tutorial on how to extend NumPy/SciPy and R by writing a wrapper for an Intel MKL function and then use it from NumPy/SciPy or R programs.</P> <P>Watch it here: <A href="http://software.intel.com/en-us/articles/powered-by-mkl-accelerating-numpy-and-scipy-performance-with-intel-mkl-python">http://software.intel.com/en-us/articles/powered-by-mkl-accelerating-numpy-and-scipy-performance-with-intel-mkl-python </A></P> Thu, 07 Nov 2013 19:36:11 GMT Zhang_Z_Intel 2013-11-07T19:36:11Z https://community.intel.com/t5/Intel-oneAPI-Math-Kernel-Library/Webinar-recording-Powered-by-MKL-Accelerating-NumPy-and-SciPy/m-p/977431#M17152 <P>The webinar recording is now available online: <A href="http://software.intel.com/en-us/articles/powered-by-mkl-accelerating-numpy-and-scipy-performance-with-intel-mkl-python">http://software.intel.com/en-us/articles/powered-by-mkl-accelerating-numpy-and-scipy-performance-with-intel-mkl-python </A></P> <P>NumPy/SciPy are scientific libraries for Python. R is a programming language for statistics computing. These tools, especially their open source packages, have gained popularity among programmers in scientific and statistic computing. A fundamental component in both NumPy/SciPy and R are the linear algebra functions, which rely on the standard BLAS and LAPACK routines. Intel® Math Kernel Library (Intel® MKL) provides a high performance implementation of BLAS and LAPACK that is optimized for Intel architectures. This webinar discusses building the open source NumPy/SciPy and R with Intel MKL to significantly improve the performance of linear algebra operations. Beyond BLAS and LAPACK, there are other functions in Intel MKL that can provide great performance benefit. This webinar will give a quick tutorial on how to extend NumPy/SciPy and R by writing a wrapper for an Intel MKL function and then use it from NumPy/SciPy or R programs.</P> <P>Watch it here: <A href="http://software.intel.com/en-us/articles/powered-by-mkl-accelerating-numpy-and-scipy-performance-with-intel-mkl-python">http://software.intel.com/en-us/articles/powered-by-mkl-accelerating-numpy-and-scipy-performance-with-intel-mkl-python </A></P> Thu, 07 Nov 2013 19:36:11 GMT https://community.intel.com/t5/Intel-oneAPI-Math-Kernel-Library/Webinar-recording-Powered-by-MKL-Accelerating-NumPy-and-SciPy/m-p/977431#M17152 Zhang_Z_Intel 2013-11-07T19:36:11Z