AI for Science Town Hall Series Kicks off at Argonne

By Tiffany Trader

August 2, 2019

Last week (July 22-23), Argonne National Lab, future home to the Intel-Cray Aurora supercomputer, hosted the first in a series of four AI for Science town hall meetings being convened by Department of Energy laboratories. The meetings are aimed at soliciting and collecting “community input on the opportunities and challenges facing the scientific community in the era of convergence of high-performance computing and artificial intelligence (AI) technologies.”

In alignment with DOE missions and the U.S. national AI initiative, the DOE community and their collaborators are being engaged to discuss broadly the opportunities that can be realized by advancing and accelerating the development of AI capabilities for science and science use cases.

Rick Stevens

“We’re asking the fundamental question: what do we have to do in the AI space to make it relevant for science? The point of the town halls is to get people thinking about what opportunities there are in different scientific domains for breakthrough science that can be accomplished by leveraging AI and working AI into simulation, bringing AI into big data, bringing AI to the facility and so forth,” said Argonne’s Rick Stevens in an interview with HPCwire. Stevens is co-chairing the town hall program along with Berkeley Lab’s Kathy Yelick and Oak Ridge Lab’s Jeff Nichols.

Each of the four town halls (held at Argonne, Oak Ridge, Berkeley, and in Washington, DC) encompasses high-level talks, application tracks and cross-cutting breakout sessions. The two-day Argonne event drew about 350 people, DOE and university researchers, primarily from the Midwest region, with about 150 people coming from other parts of the country (including broad lab participation).

The first day focused on application breakouts by science domain (e.g., chemistry, mathematics, materials, climate, biology, high energy physics, nuclear physics); on day two, participants were reoriented to cross-cutting topics, spanning fundamental math issues, software issues, data issues, understandability issues, uncertainty quantification, facilities, integration of simulation and AI, computer architecture directions, among others.

The town halls will result in an integrated report to be published by the end of the year, which will inform strategic planning, and help shape programs and budgets.

If the town hall format sounds familiar, you may recall that a series of exascale town halls was held in 2007, helping sow the seeds for the US Department of Energy’s Exascale Computing Initiative (ECI) and Exascale Computing Project (ECP). Together these activities, with a focus on codesign, application readiness and “capable exascale,” are preparing the U.S. to stand up multiple exascale-class systems in the 2021-2023 timeframe.

Learnings from the AI town halls could conceivably lead to a more targeted, and potentially funded, policy not unlike how the exascale town halls helped establish a robust national exascale program.

“We’ve got this huge exascale program and we’re now asking the question, what’s the opportunity for AI in the science space, particularly in the context of DOE but also more broadly with NIH and other agencies,” said Stevens, Argonne’s associate laboratory director for computing, environment and life sciences.

Maintaining leadership in AI is the primary directive of the U.S. national AI initiative, launched by the White House in February. The announcement and subsequent OMB budget priority letters that went out to the agencies declared progress in AI as the number one priority across the agencies.

That AI initiative also challenged agencies to come up with plans, to determine resource levels, and make progress on managing their data. It laid out a very high level blueprint as to what the country needs to do maintain progress in AI and to complement in the academic and government sector what’s going on at the internet companies, Stevens told HPCwire.

The Chicago AI for Science Town Hall at Argonne National Laboratory

“Clearly there’s huge progress in the internet space, but those Facebooks and Googles and Microsofts and Amazons and so on, those guys are not going to be the primary drivers for AI in areas like high-energy physics or nuclear energy or wind power or new materials for solar or for cancer research – it’s not their business focus,” Stevens maintained. “We recognize that the challenge is how to leverage the investments made by the private sector to build on those [advances] to add what’s missing for scientific applications — and there’s lots of things missing. And then figure out what the computing community has to do to position the infrastructure and our investments in software and algorithms and math and so on to bring the AI opportunity closer to where we currently are.”

The overarching agenda for the AI for Science town hall program includes a set of “charge questions” aimed at surfacing the most compelling problems where AI could have an impact and identifying the requirements at the research and facility level needed to realize these opportunities.

We posed one of these questions to Stevens: What are 3-5 open questions that need to be addressed to maximally contribute to AI impact in the science domains and AI impact in the enabling technologies?

His top three:

+ Uncertainty quantification, i.e. model confidence — “When you’re doing cat videos, no one cares what your confidence interval is, where your error bars are exactly, but in a scientific, a medical application, you need to know that the answer is likely to be correct.”

+ The direction of AI architectures – “Are the architectures that are being developed to accelerate general AI research – are they in fact even what we need for the types of data and the types of networks and systems we need to build for applying AI in science?”

+ Injecting AI with ground truth – “Our first way of thinking about the world is in some sense, do we have a mechanistic model of it, a physical model to simulate? And most of the progress in AI involves non-physical modeling. If you think about natural language processing, there’s no physical model for that. If you think about computer vision, most of the kinds of things that people do with computer vision, there’s no physical model; there is no ground truth that you can generate from first principles. But in many scientific areas, we’ve had 400 years of progress, in physics and chemistry and biology and so forth, and we have a lot of physical understanding. How do we use that physical understanding combined with data to build AI models that actually internalize that physical understanding? In other words, having these models be able to make predictions in the world as opposed to in some abstract space.”

The AI for Science Town Hall series continues at Oak Ridge National Laboratory (Aug. 20-21, 2019), Lawrence Berkeley National Laboratory (Sept. 11-12, 2019) and Washington DC (Oct. 22-23, 2019).

Link for more info: https://web.cvent.com/event/b03cf98d-d350-4f66-805a-1a19f03bdcf8/summary

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industry updates delivered to you every week!

MLPerf Inference 4.0 Results Showcase GenAI; Nvidia Still Dominates

March 28, 2024

There were no startling surprises in the latest MLPerf Inference benchmark (4.0) results released yesterday. Two new workloads — Llama 2 and Stable Diffusion XL — were added to the benchmark suite as MLPerf continues Read more…

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing power it brings to artificial intelligence.  Nvidia's DGX Read more…

Call for Participation in Workshop on Potential NSF CISE Quantum Initiative

March 26, 2024

Editor’s Note: Next month there will be a workshop to discuss what a quantum initiative led by NSF’s Computer, Information Science and Engineering (CISE) directorate could entail. The details are posted below in a Ca Read more…

Waseda U. Researchers Reports New Quantum Algorithm for Speeding Optimization

March 25, 2024

Optimization problems cover a wide range of applications and are often cited as good candidates for quantum computing. However, the execution time for constrained combinatorial optimization applications on quantum device Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at the network layer threatens to make bigger and brawnier pro Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HBM3E memory as well as the the ability to train 1 trillion pa Read more…

MLPerf Inference 4.0 Results Showcase GenAI; Nvidia Still Dominates

March 28, 2024

There were no startling surprises in the latest MLPerf Inference benchmark (4.0) results released yesterday. Two new workloads — Llama 2 and Stable Diffusion Read more…

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing po Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HB Read more…

Nvidia Looks to Accelerate GenAI Adoption with NIM

March 19, 2024

Today at the GPU Technology Conference, Nvidia launched a new offering aimed at helping customers quickly deploy their generative AI applications in a secure, s Read more…

The Generative AI Future Is Now, Nvidia’s Huang Says

March 19, 2024

We are in the early days of a transformative shift in how business gets done thanks to the advent of generative AI, according to Nvidia CEO and cofounder Jensen Read more…

Nvidia’s New Blackwell GPU Can Train AI Models with Trillions of Parameters

March 18, 2024

Nvidia's latest and fastest GPU, codenamed Blackwell, is here and will underpin the company's AI plans this year. The chip offers performance improvements from Read more…

Nvidia Showcases Quantum Cloud, Expanding Quantum Portfolio at GTC24

March 18, 2024

Nvidia’s barrage of quantum news at GTC24 this week includes new products, signature collaborations, and a new Nvidia Quantum Cloud for quantum developers. Wh Read more…

Alibaba Shuts Down its Quantum Computing Effort

November 30, 2023

In case you missed it, China’s e-commerce giant Alibaba has shut down its quantum computing research effort. It’s not entirely clear what drove the change. Read more…

Nvidia H100: Are 550,000 GPUs Enough for This Year?

August 17, 2023

The GPU Squeeze continues to place a premium on Nvidia H100 GPUs. In a recent Financial Times article, Nvidia reports that it expects to ship 550,000 of its lat Read more…

Shutterstock 1285747942

AMD’s Horsepower-packed MI300X GPU Beats Nvidia’s Upcoming H200

December 7, 2023

AMD and Nvidia are locked in an AI performance battle – much like the gaming GPU performance clash the companies have waged for decades. AMD has claimed it Read more…

DoD Takes a Long View of Quantum Computing

December 19, 2023

Given the large sums tied to expensive weapon systems – think $100-million-plus per F-35 fighter – it’s easy to forget the U.S. Department of Defense is a Read more…

Synopsys Eats Ansys: Does HPC Get Indigestion?

February 8, 2024

Recently, it was announced that Synopsys is buying HPC tool developer Ansys. Started in Pittsburgh, Pa., in 1970 as Swanson Analysis Systems, Inc. (SASI) by John Swanson (and eventually renamed), Ansys serves the CAE (Computer Aided Engineering)/multiphysics engineering simulation market. Read more…

Choosing the Right GPU for LLM Inference and Training

December 11, 2023

Accelerating the training and inference processes of deep learning models is crucial for unleashing their true potential and NVIDIA GPUs have emerged as a game- Read more…

Intel’s Server and PC Chip Development Will Blur After 2025

January 15, 2024

Intel's dealing with much more than chip rivals breathing down its neck; it is simultaneously integrating a bevy of new technologies such as chiplets, artificia Read more…

Baidu Exits Quantum, Closely Following Alibaba’s Earlier Move

January 5, 2024

Reuters reported this week that Baidu, China’s giant e-commerce and services provider, is exiting the quantum computing development arena. Reuters reported � Read more…

Leading Solution Providers

Contributors

Comparing NVIDIA A100 and NVIDIA L40S: Which GPU is Ideal for AI and Graphics-Intensive Workloads?

October 30, 2023

With long lead times for the NVIDIA H100 and A100 GPUs, many organizations are looking at the new NVIDIA L40S GPU, which it’s a new GPU optimized for AI and g Read more…

Shutterstock 1179408610

Google Addresses the Mysteries of Its Hypercomputer 

December 28, 2023

When Google launched its Hypercomputer earlier this month (December 2023), the first reaction was, "Say what?" It turns out that the Hypercomputer is Google's t Read more…

AMD MI3000A

How AMD May Get Across the CUDA Moat

October 5, 2023

When discussing GenAI, the term "GPU" almost always enters the conversation and the topic often moves toward performance and access. Interestingly, the word "GPU" is assumed to mean "Nvidia" products. (As an aside, the popular Nvidia hardware used in GenAI are not technically... Read more…

Shutterstock 1606064203

Meta’s Zuckerberg Puts Its AI Future in the Hands of 600,000 GPUs

January 25, 2024

In under two minutes, Meta's CEO, Mark Zuckerberg, laid out the company's AI plans, which included a plan to build an artificial intelligence system with the eq Read more…

Google Introduces ‘Hypercomputer’ to Its AI Infrastructure

December 11, 2023

Google ran out of monikers to describe its new AI system released on December 7. Supercomputer perhaps wasn't an apt description, so it settled on Hypercomputer Read more…

China Is All In on a RISC-V Future

January 8, 2024

The state of RISC-V in China was discussed in a recent report released by the Jamestown Foundation, a Washington, D.C.-based think tank. The report, entitled "E Read more…

Intel Won’t Have a Xeon Max Chip with New Emerald Rapids CPU

December 14, 2023

As expected, Intel officially announced its 5th generation Xeon server chips codenamed Emerald Rapids at an event in New York City, where the focus was really o Read more…

IBM Quantum Summit: Two New QPUs, Upgraded Qiskit, 10-year Roadmap and More

December 4, 2023

IBM kicks off its annual Quantum Summit today and will announce a broad range of advances including its much-anticipated 1121-qubit Condor QPU, a smaller 133-qu Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire