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Fujitsu to Build 37 Petaflop AI Supercomputer for AIST in Japan

Nikkei in Japan reports that Fujitsu is building a 37 Petaflop supercomputer for the National Institute of Advanced Industrial Science and Technology (AIST). “Targeted at Deep Learning workloads, the machine will power the AI research center at the University of Tokyo’s Chiba Prefecture campus. The new Fujitsu system feature will comprise 1,088 servers, 2,176 Intel Xeon processors, and 4,352 NVIDIA GPUs.”

Predicting Earthquakes with Machine Learning

Researchers at LANL are using Machine Learning to predict earthquakes. “The novelty of our work is the use of machine learning to discover and understand new physics of failure, through examination of the recorded auditory signal from the experimental setup. I think the future of earthquake physics will rely heavily on machine learning to process massive amounts of raw seismic data. Our work represents an important step in this direction.”

TensorFlow Deep Learning Optimized for Modern Intel Architectures

Researchers at Google and Intel recently collaborated to extract the maximum performance from Intel® Xeon and Intel® Xeon Phi processors running TensorFlow*, a leading deep learning and machine learning framework. This effort resulted in significant performance gains and leads the way for ensuring similar gains from the next generation of products from Intel. Optimizing Deep Neural Network (DNN) models such as TensorFlow presents challenges not unlike those encountered with more traditional High Performance Computing applications for science and industry.

Machine Learning Technology: A Guide to Scaling Up and Out

Frameworks, applications, libraries and toolkits—journeying through the world of deep learning can be daunting. If you’re trying to decide whether or not to begin a machine or deep learning project, there are several points that should first be considered. This is the second article in a five-part series that covers the steps to take before launching a machine learning startup. This article covers popular machine learning technology.

Launch a Machine Learning Startup

Launch a Machine Learning Startup – In this report, we’ll address everything from how to choose a framework and pick the tools you need to get started, to the questions you’ll be asking yourself, and the benefits of immersing yourself in the machine and deep learning communities. This report also untangles the jargon and explores what these terms actually mean. Download this special report now.

What Developers Need to Consider When Exploring Machine Learning

Frameworks, applications, libraries and toolkits—journeying through the world of deep learning can be daunting. If you’re trying to decide whether or not to begin a machine or deep learning project, there are several points that should first be considered. This is the first article in a five-part series that covers the steps to take before launching a machine learning startup. 

Radio Free HPC Looks at AI Ethics and a Tale of Henry’s Super Heroism

In this podcast, the Radio Free HPC team learns about Henry’s first exploit as an Ethical Superhero. “After witnessing a hit-and-run fender bender, Henry confronted the culprit and ensured that the miscreant left a note on the victim’s windshield. And while we applaud Henry for his heroism, we are also very grateful that he was not shot in the process. This tale leads us into a discussion of AI ethics and how we won’t have this problem in the coming era of self-driving cars.”

Go with Intel® Data Analytics Acceleration Library and Go*

Use of the Go* programming language and it’s developer community has grown significantly since it’s official launch by Google in 2009. Like many popular programming languages (C and Java come to mind), Go started as an experiment to design a new programming language that would fix some of the common problems of other languages and yet stay true to the basic tenets of modern programming: be scalable, productive, readable, enable robust development environments, and support networking and multiprocessing.

Nvidia Volta GPUs Power HPC & AI at ISC 2017

In this video from ISC 2017, Keith Morris from NVIDIA describes how the company’s next-generation Volta GPU technology will drive HPC and AI applications to new levels of performance. “GV100 not only builds upon the advances of its predecessor, the Pascal GP100 GPU, it significantly improves performance and scalability, and adds many new features that improve programmability. These advances will supercharge HPC, data center, supercomputer, and deep learning systems and applications.”

DDN: Enabling Scientific Discovery and Exascale Initiatives

Alex Bouzari gave this talk at the DDN User Group. “The goal of the event is to gather the community during ISC to discover how HPC organizations are assessing and leveraging technology to raise the bar on HPC innovations and best practices. From exciting user presentations to engaging roundtable conversations and groundbreaking technology updates, this can’t-miss event delivers the ideas and inspiration to help your cutting-edge HPC initiatives transform the world.”