In interpreted languages, it just takes longer to get stuff done - I earlier gave the example where the Python source code a = b + c would result in a BINARY_ADD byte code which takes 78 machine instructions to do the add, but it's a single native ADD instruction if run in compiled language like C or C++. How can we speed this up? Or as the performance expert would say, how do I decrease...
Caffe is a deep learning framework developed by the Berkeley Vision and Learning Center (BVLC) and one of the most popular community frameworks for image recognition. Caffe is often used as a benchmark together with AlexNet*, a neural network topology for image recognition, and ImageNet*, a database of labeled images.
Intel® Parallel Studio XE is a very popular product from Intel that includes the Intel® Compilers, Intel® Performance Libraries, tools for analysis, debugging and tuning, tools for MPI and the Intel® MPI Library. Did you know that some of these are available for free? Here is a guide to “what is available free” from the Intel Parallel Studio XE suites.
As part of a long-term commitment to enhance ease-of-use, the OpenStack UX project, with support of the OpenStack Foundation and the Technical Committee, is now bu
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.
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.