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The AI Opportunity

The tremendous growth in compute power and explosion of data is leading every industry to seek AI-based solutions. In this Tech.Decoded video, “The AI Opportunity – Episode 1: The Compute Power Difference,” Vice President of Intel Architecture and AI expert Wei Li shares his views on the opportunities and challenges in AI for software developers, how Intel is supporting their efforts, and where we’re heading next.

Fast-track Application Performance and Development with Intel® Performance Libraries

Intel continues its strident efforts to refine libraries optimized to yield the utmost performance from Intel® processors. The Intel® Performance Libraries provide a large collection of prebuilt and tested, performance-optimized functions to developers. By utilizing these libraries, developers can reduce the costs and time associated with software development and maintenance, and focus efforts on their own application code.

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.