The server world has really embraced Python in a big way. For example, the OpenStack project is a very popular Infrastructure as a Service offering, and most of it is written in Python. This makes Python a leader for Software Defined Infrastructure (SDI), Software Defined Storage (SDS) and Software Defined Networking (SDN).
Intel® for its part invests countless hours and billions of transistors to add features in our silicon products which will speed up people's lives. If only they knew how to take advantage of it! Part of our job in dynamic languages is what I call "putting the cookies on the bottom shelf". Make this advanced technology easily consumable, and show you the value of it so you can be sure to use it.
The core components of the Internet get updated constantly. Every time the source changes, the health and performance can change. A single source code change can fail to build, can break compatibility with existing code and can change the performance anywhere from a fraction of a percent up to 10% or more on major customer workloads. We're trying to read the pulse of our core components (Python,...
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.
My current gig is mostly about performance. I manage a group of software engineers dedicated to the languages becoming really important to the cloud and the datacenter.
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.
Using Intel® Data Analytics Acceleration Library to Improve the Performance of Naïve Bayes Algorithm in Python*This article discusses machine learning and describes a machine learning method/algorithm called Naïve Bayes (NB) . It also describes how to use Intel® Data Analytics Acceleration Library (Intel® DAAL)  to improve the performance of an NB algorithm.