DataScience

It's no secret that we produce extraordinary amounts of data every day, necessitating the need for performant, scalable algorithms that can process it all. Explore the range of Intel data analytics tools, all optimized for Big Data hardware and software stacks.

What is Graph Analytics and why does it matter? Short answer: Because it touches a huge swath of our daily lives. Find out how new Intel® tools simplify getting objective, reproducible graph analytics performance data without obfuscation.

There are a variety of algorithms to extract useful info from a graph. Explore the implementation characteristics of basic graph analysis algorithms and how they perform on Intel® Xeon® processors.

There are three ways to efficiently achieve parallelism in Python*: dispatch your own code, rely on a library, use a framework. This article looks at the third way … using Numba* to generate native-speed code.

Meet Intel’s optimizations for frameworks like Caffe* and TensorFlow* and see how Intel® Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN)helps speed training and inference execution with no code changes.

Field programmable gate arrays (FPGAs) not only are capable of high performance, they’re wonderfully malleable—configurable to match the application rather than the other way around. Because of this freedom, FPGA app developers have come to rely on tools and languages (specifically HDLs) used by hardware designers, rather than software programmers. But that’s changing. Find out the Intel® FPGA SDK for OpenCL™ technology provides an alternative to HDL programming.