In this contributed article, tech writer Sean Thielen describes how Kx crunches a 1.2 billion record database of New York City taxi cab ride data to demonstrate what the Intel Xeon Phi processor could mean to distributed big data processing.
Kx Streaming Analytics Demo Easily Crunches 1.2 Billion NYC Taxi Data Points Using Standard Intel Xeon Phi
In this video from SC16, Intel demonstrates how Altera FPGAs can accelerate Machine Learning applications with greater power efficiency. “The demo was put together using OpenCL design tools and then compiled to FPGA. From an end-user perspective, they tied it together using Intel MKL-DNN with CAFFE on top of that. This week, Intel announced the DLIA Deep Learning Inference Accelerator that brings the whole solution together in a box.”
In this video from the Intel HPC Developer Conference, Noah Rosenberg and Karl Stiefvater from Pikazo describe the company’s innovative Pikazo App for smartphones. “Pikazo was developed in 2015 using neural style transfer algorithms. It is a collaboration between human, machine, and our concept of art. It is a universal art machine that paints any image in the style of any other, producing sometimes-beautiful, sometimes-disturbing, always-surprising artworks. Pikazo allows novice artists to create impressive imagery via a technique known as neural style transfer.”
Intel Corporation announced a range of new products, technologies and investments from the edge to the data center to help expand and accelerate the growth of artificial intelligence (AI).
In this video from the Intel HPC Developer Conference, Franz Kiraly from Imperial College London and the Alan Turing Institute describes why many companies and organizations are beginning to scope their potential for applying rigorous quantitative methodology and machine learning.
In this video from the Intel HPC Developer Conference, Elmoustapha Ould-ahmed-vall from Intel describes how the company is doubling down to optimize Machine Learning frameworks for Intel Platforms. Using open source frameworks as a starting point, surprising speedups are possible using Intel technologies.
The insideBIGDATA Guide to Healthcare & Life Sciences is a useful new resource directed toward enterprise thought leaders who wish to gain strategic insights into this exciting new area of technology. This segment focuses on the rise of deep learning.
The insideBIGDATA Guide to Healthcare & Life Sciences is a useful new resource directed toward enterprise thought leaders who wish to gain strategic insights into this exciting new area of technology. The guide provides an overview of the utilization of big data technologies as an emerging discipline in healthcare and life sciences. It explores the characteristics of this business strategy and the benefits of leveraging big data technologies within these sectors. It also touches on the challenges and future directions of big data and analytics in the healthcare and life sciences industries.
“Benchmarks, customer experiences, and the technical literature have shown that code modernization can greatly increase application performance on both Intel Xeon and Intel Xeon Phi processors. Colfax Research recently published a study showing that image tagging performance using the open source NeuralTalk2 software can be improved 28x on Intel Xeon processors and by over 55x on the latest Intel Xeon Phi processors.”
In this special guest feature, Rob Farber from TechEnablement writes that the Intel Scalable Systems Framework is pushing the boundaries of Machine Learning performance. “machine learning and other data-intensive HPC workloads cannot scale unless the storage filesystem can scale to meet the increased demands for data.”