One of the Intel® Modern Code Developer Challenge winners, Daniel Falguera, describes many of the optimizations he implemented and why some didn't work.
This paper demonstrates a special version of Caffe* — a deep learning framework originally developed by the Berkeley Vision and Learning Center (BVLC) — that is optimized for Intel® architecture.
See how the new Intel® Advanced Vector Extensions 512CD and the Intel AVX512F subsets (available in the Intel® Xeon Phi processor and in future Intel Xeon processors) lets the compiler automatically generate vector code with no changes to the code.
Cython* is a superset of Python* that additionally supports C functions and C types on variable and class attributes. Cython generates C extension modules, which can be used by the main Python program using the import statement.
Learn more about an in-depth analysis of code modernization performance conducted by optimizing original CPU code and re-running tests on the latest GPU/CPU hardware.