“The IO infrastructure of TSUBAME3.0 combines fast in-node NVMe SSDs and a large, fast, Lustre-based system from DDN. The 15.9PB Lustre* parallel file system, composed of three of DDN’s high-end ES14KX storage appliances, is rated at a peak performance of 150GB/s. The TSUBAME collaboration represents an evolutionary branch of HPC that could well develop into the dominant HPC paradigm at about the time the most advanced supercomputing nations and consortia achieve Exascale computing.”
“Servers today have hundreds of knobs that can be tuned for performance and energy efficiency. While some of these knobs can have a dramatic effect on these metrics, manually tuning them is a tedious task. It is very labor intensive, it requires a lot of expertise, and the tuned settings are only relevant for the hardware and software that were used in the tuning process. In addition to that, manual tuning can’t take advantage of application phases that may each require different settings. In this presentation, we will talk about the concept of dynamic tuning and its advantages. We will also demo how to improve performance using manual tuning as well as dynamic tuning using DatArcs Optimizer.”
“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.”
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.
“As with all new technology, developers will have to create processes in order to modernize applications to take advantage of any new feature. Rather than randomly trying to improve the performance of an application, it is wise to be very familiar with the application and use available tools to understand bottlenecks and look for areas of improvement.”
“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.”
“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.”
“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.”
The OpenFog Consortium was founded over one year ago to accelerate adoption of fog computing through an open, interoperable architecture. The newly published OpenFog Reference Architecture is a high-level framework that will lead to industry standards for fog computing. The OpenFog Consortium is collaborating with standards development organizations such as IEEE to generate rigorous user, functional and architectural requirements, plus detailed application program interfaces (APIs) and performance metrics to guide the implementation of interoperable designs.
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.”