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NVIDIA P100 GPUs come to Google Cloud Platform

Today the good folks at the Google Compute Platform announced the availability of NVIDIA GPUs in the Cloud for multiple geographies. Cloud GPUs can accelerate workloads such as machine learning training and inference, geophysical data processing, simulation, seismic analysis, molecular modeling, genomics and many more high performance compute use cases. “Today, we’re happy to make some massively parallel announcements for Cloud GPUs. First, Google Cloud Platform (GCP) gets another performance boost with the public launch of NVIDIA P100 GPUs in beta.

NASA Perspectives on Deep Learning

Nikunj Oza from NASA Ames gave this talk at the HPC User Forum. “This talk will give a broad overview of work at NASA in the space of data sciences, data mining, machine learning, and related areas at NASA. This will include work within the Data Sciences Group at NASA Ames, together with other groups at NASA and university and industry partners. We will delineate our thoughts on the roles of NASA, academia, and industry in advancing machine learning to help with NASA problems.”

JAIST in Japan installs Cray XC40 Supercomputer

Today Cray announced the Japan Advanced Institute for Science and Technology (JAIST) has put a Cray XC40 supercomputer into production. The Cray XC40 supercomputers incorporate the Aries high performance network interconnect for low latency and scalable global bandwidth, as well as the latest Intel Xeon processors, Intel Xeon Phi processors, and NVIDIA Tesla GPU accelerators. “Our new Cray XC40 supercomputer will support our mission of becoming a premier center of excellence in education and research.”

GPUs Accelerate Population Distribution Mapping Around the Globe

With the Earth’s population at 7 billion and growing, understanding population distribution is essential to meeting societal needs for infrastructure, resources and vital services. This article highlights how NVIDIA GPU-powered AI is accelerating mapping and analysis of population distribution around the globe. “If there is a disaster anywhere in the world,” said Bhaduri, “as soon as we have imaging we can create very useful information for responders, empowering recovery in a matter of hours rather than days.”

Machine & Deep Learning: Practical Deployments and Best Practices for the Next Two Years

Arno Kolster from Providentia Worldwide gave this talk at the HPC User Forum in Milwaukee. “Providentia Worldwide is a new venture in technology and solutions consulting which bridges the gap between High Performance Computing and Enterprise Hyperscale computing. We take the best practices from the most demanding compute environments in the world and apply those techniques and design patterns to your business.”

New PGI 17.7 Release Supports NVIDIA Volta GPUs

Today NVIDIA released Version 17.7 of PGI 2017 Compilers and Tools, delivering improved performance and programming simplicity to HPC developers who target multicore CPUs and heterogeneous GPU-accelerated systems.

Benefits of Multi-rail Cluster Architectures for GPU-based Nodes

Craig Tierney from NVIDIA gave this talk at the MVAPICH User Group meeting. “As high performance computing moves toward GPU-accelerated architectures, single node application performance can be between 3x and 75x faster than the CPUs alone. Performance increases of this size will require increases in network bandwidth and message rate to prevent the network from becoming the bottleneck in scalability. In this talk, we will present results from NVLink enabled systems connected via quad-rail EDR Infiniband.”

Solving AI Hardware Challenges

For many deep learning startups out there, buying AI hardware and a large quantity of powerful GPUs is not feasible. So many of these startup companies are turning to cloud GPU computing to crunch their data and run their algorithms. Katie Rivera, of One Stop Systems, explores some of the AI hardware challenges that can arise, as well as the new tools designed to tackle these issues. 

New OrionX Survey: Insights in Artificial Intelligence

In this Radio Free HPC podcast, Dan Olds and Shahin Khan from OrionX describe their new AI Survey. “OrionX Research has completed one the most comprehensive surveys to date of Artificial Intelligence, Machine Learning, and Deep Learning. With over 300 respondents in North America, representing 13 industries, our model indicates a confidence level of 95% and a margin of error of 6%. Covering 144 questions/data points, it provides a comprehensive view of what customers are doing and planning to do with AI/ML/DL.”

Call for Participation: GTC 2018 in San Jose

The GPU Technology Conference (GTC 2018) has issued their Call for Participation. The event takes place March 26-29 in San Jose, California. “Don’t miss this unique opportunity to participate in the world’s most important GPU event, NVIDIA’s GPU Technology Conference (GTC 2018). Sign up to present a talk, poster, or lab on how GPUs power the most dynamic areas in computing today—including AI and deep learning, big data analytics, healthcare, smart cities, IoT, HPC, VR, and more.”