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Video: Pure Storage Speeds I/O for AI & HPC Workloads at SC17

In this video from SC17 in Denver, Brian Gold from Pure Storage describes how the company’s innovative FlashBlade Systems speed up I/O for AI & HPC workloads. “From scientific research and movie rendering to artificial intelligence, applications push the limits on thousands of GPU cores or thousands of CPU servers. Parallel compute demands parallel storage. FlashBlade is the industry’s first all-flash storage purpose-built for modern analytics – architected from the ground-up to deliver a powerful cloud-era data platform that’s fast, big, and simple.”

For HPC and Deep Learning, GPUs are here to stay

In this special guest feature from Scientific Computing World, David Yip, HPC and Storage Business Development at OCF, provides his take on the place of GPU technology in HPC. “Using GPUs in the HPC datacenter in place of CPUs can dramatically increase the power requirements needed, but if your computational performance goes through the roof, then I’d argue it’s a trade-off worth making.”

HPE Steps Up with AI Innovation at SC17

Today at SC17, Hewlett Packard Enterprise announced new high-density compute and storage solutions that enable enterprises to harness the power of high performance computing and artificial intelligence applications. “Today, HPE is augmenting its proven supercomputing and large commercial HPC and AI capabilities with new high-density compute and storage solutions to accelerate market adoption by enabling organizations of all sizes to address challenges in HPC, big data, object storage and AI with more choice and flexibility.”

SC17 Preview: Artificial Intelligence and The Virtuous Cycle of Compute

In this video, Pradeep Dubey from Intel Labs describes his upcoming SC17 Invited Talk on Artificial Intelligence. “Dubey will discuss how the convergence of AI, Big Data, HPC systems, and algorithmic advances are transforming the relationship between computers and humans, disrupting past notions of a partnership where humans made all the “intelligent” decisions.”

Advancing the Financial Services Industry Through Machine Learning

As financial institutions look to be empowered through machine learning, they should first acknowledge the benefits, challenges, and considerations involved. Download the new insideHPC guide that is essential reading for anyone involved in the financial services industry, from those who are beginning to explore the potential of machine learning, to those looking to expand and maximize its use. 

Intel and the Coming AI Revolution

In this video from the Intel HPC Developer Conference, Gadi Singer from Intel describes how the company is moving forward with Artificial Intelligence. “We are deeply committed to unlocking the promise of AI: conducting research on neuromorphic computing, exploring new architectures and learning paradigms.”

Video: Applying AI to Science

In this video from the Intel HPC Developer Conference, Prabhat from NERSC describes how AI applies to science. “Looking ahead, Prabhat sees broad applications for deep learning in scientific research beyond climate science—especially in astronomy, cosmology, neuroscience, material science, and physics.”

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.