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Interview: Cray’s Steve Scott on What’s Next for Supercomputing

In this video from KAUST, Steve Scott from at Cray explains where supercomputing is going and why there is a never-ending demand for faster and faster computers. Responsible for guiding Cray’s long term product roadmap in high-performance computing, storage and data analytics, Mr. Scott is chief architect of several generations of systems and interconnects at Cray.

Radio Free HPC Gets the Scoop from Dan’s Daughter in Washington, D.C.

In this podcast, the Radio Free HPC team hosts Dan’s daughter Elizabeth. How did Dan get this way? We’re on a mission to find out even as Elizabeth complains of the early onset of Curmudgeon’s Syndrome. Somehow she has turned out well, though, and has a great gig with an Oil company in Washington, D.C., so we also get her take on what is going on in the Nation’s capital.

Deep Learning & HPC: New Challenges for Large Scale Computing

“In recent years, major breakthroughs were achieved in different fields using deep learning. From image segmentation, speech recognition or self-driving cars, deep learning is everywhere. Performance of image classification, segmentation, localization have reached levels not seen before thanks to GPUs and large scale GPU-based deployments, leading deep learning to be a first class HPC workload.”

ISC 2017 Distinguished Talks to Focus on Data Analytics in Manufacturing & Science

Today ISC 2017 announced that it’s Distinguished Talk series will focus on Data Analytics in manufacturing and scientific applications. One of the Distinguished Talks will be given by Dr. Sabine Jeschke from the Cybernetics Lab at the RWTH Aachen University on the topic of, “Robots in Crowds – Robots and Clouds.” Jeschke’s presentation will be followed by one from physicist Kerstin Tackmann, from the German Electron Synchrotron (DESY) research center, who will discuss big data and machine learning techniques used for the ATLAS experiment at the Large Hadron Collider.

IBM Machine Learning Platform Comes to the Private Cloud

“Machine Learning and deep learning represent new frontiers in analytics. These technologies will be foundational to automating insight at the scale of the world’s critical systems and cloud services,” said Rob Thomas, General Manager, IBM Analytics. “IBM Machine Learning was designed leveraging our core Watson technologies to accelerate the adoption of machine learning where the majority of corporate data resides. As clients see business returns on private cloud, they will expand for hybrid and public cloud implementations.”

Defining AI, Machine Learning, and Deep Learning

“In this guide, we take a high-level view of AI and deep learning in terms of how it’s being used and what technological advances have made it possible. We also explain the difference between AI, machine learning and deep learning, and examine the intersection of AI and HPC. We also present the results of a recent insideBIGDATA survey to see how well these new technologies are being received. Finally, we take a look at a number of high-profile use case examples showing the effective use of AI in a variety of problem domains.”

Podcast: Democratizing Education for the Next Wave of AI

“Coursera has named Intel as one of its first corporate content partners. Together, Coursera and Intel will develop and distribute courses to democratize access to artificial intelligence and machine learning. In this interview, Ibrahim talks about her and Coursera’s history, reports on Coursera’s progress delivering education at massive scale, and discusses Coursera and Intel’s unique partnership for AI.”

Video: Computing of the Future

Jeffrey Welser from IBM Research Almaden presented this talk at the Stanford HPC Conference. “Whether exploring new technical capabilities, collaborating on ethical practices or applying Watson technology to cancer research, financial decision-making, oil exploration or educational toys, IBM Research is shaping the future of AI.”

Supercomputing Transportation System Data using TACC’s Rustler

Over at TACC, Faith Singer-Villalobos writes that researchers are using the Rustler supercomputer to tackle Big Data from self-driving connected vehicles (CVs). “The volume and complexity of CV data are tremendous and present a big data challenge for the transportation research community,” said Natalia Ruiz-Juri, a research associate with The University of Texas at Austin’s Center for Transportation Research. While there is uncertainty in the characteristics of the data that will eventually be available, the ability to efficiently explore existing datasets is paramount.

Overcoming the Learning Curve of New Processor Architectures

High-performance computing (HPC) tools are helping financial firms survive and thrive in this highly demanding and data-intensive industry. As financial models grow in complexity and greater amounts of data must be processed and analyzed on a daily basis, firms are increasingly turning to HPC solutions to exploit the latest technology performance improvements. Suresh Aswani, Senior Manager, Solutions Marketing, at Hewlett Packard Enterprise, shares how to overcome the learning curve of new processor architectures.