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Argonne Publishes AI for Science Report

Argonne National Lab has published a comprehensive AI for Science Report based on a series of Town Hall meetings held in 2019. Hosted by Argonne, Oak Ridge, and Berkeley National Laboratories, the four town hall meetings were attended by more than 1,000 U.S. scientists and engineers. The goal of the town hall series was to examine scientific opportunities in the areas of artificial intelligence (AI), Big Data, and high-performance computing (HPC) in the next decade, and to capture the big ideas, grand challenges, and next steps to realizing these opportunities.

MLPerf-HPC Working Group seeks participation

In this special guest feature, Murali Emani from Argonne writes that a team of scientists from DoE labs have formed a working group called MLPerf-HPC to focus on benchmarking machine learning workloads for high performance computing. “As machine learning (ML) is becoming a critical component to help run applications faster, improve throughput and understand the insights from the data generated from simulations, benchmarking ML methods with scientific workloads at scale will be important as we progress towards next generation supercomputers.”

LBNL Breaks New Ground in Data Center Optimization

Berkeley Lab has been at the forefront of efforts to design, build, and optimize energy-efficient hyperscale data centers. “In the march to exascale computing, there are real questions about the hard limits you run up against in terms of energy consumption and cooling loads,” Elliott said. “NERSC is very interested in optimizing its facilities to be leaders in energy-efficient HPC.”

Exascale Computing Project Announces Staff Changes Within Software Technology Group

The US Department of Energy’s Exascale Computing Project (ECP) has announced the following staff changes within the Software Technology group. Lois Curfman McInnes from Argonne will replace Jonathan Carter as Deputy Director for Software Technology. Meanwhile Sherry Li is now team lead for Math Libraries. “We are fortunate to have such an incredibly seasoned, knowledgeable, and respected staff to help us lead the ECP efforts in bringing the nation’s first exascale computing software environment to fruition,” said Mike Heroux from Sandia National Labs.

Stepping up Qubit research at the DOE

To use quantum computers on a large scale, we need to improve the technology at their heart – qubits. Qubits are the quantum version of conventional computers’ most basic form of information, bits. The DOE’s Office of Science is supporting research into developing the ingredients and recipes to build these challenging qubits.

Podcast: Rewriting NWChem for Exascale

In this Let’s Talk Exascale podcast, researchers from the NWChemEx project team describe how they are readying the popular code for Exascale. The NWChemEx team’s most significant success so far has been to scale coupled-cluster calculations to a much larger number of processors. “In NWChem we had the global arrays as a toolkit to be able to build parallel applications.”

Podcast: Supercomputing the Human Microbiome

In this Let’s Talk Exascale podcast, Kathy Yelick and Lenny Oliker from LBNL describe how the ExaBiome project is developing computational tools to analyze microbial species—bacteria or viruses that typically live in communities of hundreds of different species. “Pushing past the traditional shared-memory-system approach, the ExaBiome team has developed efficient distributed memory implementations and analyzed some of the largest datasets in the metagenomics community.”

Sandia and LBNL to lead Quantum Information Edge Strategic Alliance

A nationwide alliance of national labs, universities, and industry launched today to advance the frontiers of quantum computing systems designed to solve urgent scientific challenges and maintain U.S. leadership in next-generation information technology. “The Quantum Information Edge will accelerate quantum R&D by simultaneously pursuing solutions across a broad range of science and technology areas, and integrating these efforts to build working quantum computing systems that benefit the nation and science.”

Deep Learning on Summit Supercomputer Powers Insights for Nuclear Waste Remediation

A research collaboration between LBNL, PNNL, Brown University, and NVIDIA has achieved exaflop (half-precision) performance on the Summit supercomputer with a deep learning application used to model subsurface flow in the study of nuclear waste remediation. Their achievement, which will be presented during the “Deep Learning on Supercomputers” workshop at SC19, demonstrates the promise of physics-informed generative adversarial networks (GANs) for analyzing complex, large-scale science problems.

Podcast: ExaStar Project Seeks Answers in Cosmos

In this podcast, Daniel Kasen from LBNL and Bronson Messer of ORNL discuss advancing cosmology through EXASTAR, part of the Exascale Computing Project. “We want to figure out how space and time get warped by gravitational waves, how neutrinos and other subatomic particles were produced in these explosions, and how they sort of lead us down to a chain of events that finally produced us.”