Big Data requires processing huge amounts of data. Intel Advanced Vector Extensions 2 (aka AVX2) promoted most Intel AVX 128-bits integer SIMD instruction sets to 256-bits.
Docker* is an emerging technology that has become very popular recently in the market. It provides a flexible architecture to deploy applications. OpenStack* is another hot technology on the market. It has been available for several years, became more stable and also added more features support in recent releases.
The Intel® Parallel Computing Center (Intel® PCC) on Big Data in Biosciences and Public Health is focused on developing and optimizing parallel algorithms and software on Intel® Xeon® Processor and Intel® Xeon Phi™ Coprocessor systems for handling high-throughput DNA sequencing data and gene expression data.
Foundations of Digital Games is a summit of innovators and influencers in gaming-related academia as well as the games industry itself. In what was originally “the premier educational conference for faculty who use game development to teach computer science concepts and principles”, it began in 2006 as Microsoft Academic Days on Game Development in Computer Science Education (GDCSE) and was...
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.
This Technology Insight will demonstrate how to optimize data analytics and machine learning workloads for Intel® Architecture based data center platforms. Speaker: Pradeep Dubey Intel Fellow, Intel Labs Director, Parallel Computing Lab, Intel Corporation
A number of usage models are possible given the flexible interfaces provided by the Cache Allocation Technology (CAT) feature, including prioritization of important applications and isolation of applications to reduce interference.
Cache Allocation Technology (CAT) provides benefits across a number of usages, as described in the previous article in this series. This article briefly describes one proof point from the data center (prioritizing a web server to improve its performance) and one from communications (protecting a key communications infrastructure virtual machine (VM)).