Sign up for our newsletter and get the latest big data news and analysis.

Intel’s New Processors: A Machine-learning Perspective

Machine learning and its younger sibling deep learning are continuing their acceleration in terms of increasing the value of enterprise data assets across a variety of problem domains. A recent talk by Dr. Amitai Armon, Chief Data-Scientist of Intel’s Advanced Analytics department, at the O’reilly Artificial Intelligence conference, New-York, September 27 2016, focused on the usage of Intel’s new server processors for various machine learning tasks as well as considerations in choosing and matching processors for specific machine learning tasks.

Building Fast Data Compression Code for Cloud and Edge Applications

Finding efficient ways to compress and decompress data is more important than ever. Compressed data takes up less space and requires less time and network bandwidth to transfer. In this article, we’ll discuss the data compression functions and the latest improvements in the Intel® Integrated Performance Primitives (Intel® IPP) library.

Solutions for Autonomous Driving – From Car to Cloud

From car to cloud―and the connectivity in between―there is a need for automated driving solutions that include high-performance platforms, software development tools, and robust technologies for the data center. With Intel GO automotive driving solutions, Intel brings its deep expertise in computing, connectivity, and the cloud to the automotive industry.

The Importance of Vectorization Resurfaces

Vectorization offers potential speedups in codes with significant array-based computations—speedups that amplify the improved performance obtained through higher-level, parallel computations using threads and distributed execution on clusters. Key features for vectorization include tunable array sizes to reflect various processor cache and instruction capabilities and stride-1 accesses within inner loops.

Julia: A High-Level Language for Supercomputing and Big Data

Julia is a new language for technical computing that is meant to address the problem of language environments not designed to run efficiently on large compute clusters. It reads like Python or Octave, but performs as well as C. It has built-in primitives for multi-threading and distributed computing, allowing applications to scale to millions of cores. In addition to HPC, Julia is also gaining traction in the data science community.

Taking Control of System Storage Performance

The Intel Storage Performance Snapshot Tool gives you a fast, high-level look at system storage performance and helps you understand the potential benefits of moving to faster storage. To demonstrate the power of this tool, let’s consider two snapshots while running a MySQL database workload against the same system but with two storage configurations.

Case Study: More Efficient Numerical Simulation in Astrophysics

Novosibirsk State University is one of the major research and educational centers in Russia and one of the largest universities in Siberia. When researchers at the University were looking to develop and optimize a software tool for numerical simulation of magnetohydrodynamics (MHD) problems with hydrogen ionization —part of an astrophysical objects simulation (AstroPhi) project—they needed to optimize the tool’s performance on Intel® Xeon Phi™ processor-based hardware.

Go for Big Data

Many of the hottest modern infrastructure projects are powered by the Go programming language, including Kubernetes, Docker, Consul, etcd, and many more. Go is turning into a go to language for devops, web servers, and microservices. One route to providing robust, performant, and scalable data processing within Go is to utilize the Intel® Data Analytics Acceleration Library (Intel® DAAL) within Go programs.

OpenVX – The Standard for Computer Vision

OpenVX is an API enabling software developers to add hardware accelerated computer vision capabilities to their programs. Coupled with the current upswing in the use of deep learning technologies, computer vision applications with OpenVX are becoming very important. OpenVX is an integral part of Intel Computer Vision SDK. This comprehensive toolkit is for developing and deploying vision-oriented solutions on platforms from Intel, including autonomous vehicles, digital surveillance cameras, robotics, and mixed-reality headsets.

7 Steps From Raw Data to Insight

Data scientists generally ascribe to the “machine learning process” which is seen as a roadmap to follow when working on a data science project. The infographic at the end of this article provides a detailed work flow that it is general enough to encompass pretty much any data science project.