Preparing for Exascale: Argonne’s Aurora to Accelerate Discoveries in Particle Physics at CERN

July 22, 2021

July 22, 2021 — The U.S. Department of Energy’s (DOE) Argonne National Laboratory will be home to one of the nation’s first exascale supercomputers when Aurora arrives in 2022. To prepare codes for the architecture and scale of the system, 15 research teams are taking part in the Aurora Early Science Program through the Argonne Leadership Computing Facility (ALCF), a DOE Office of Science User Facility. With access to pre-production time on the supercomputer, these researchers will be among the first in the world to use an exascale machine for science.

Early philosophers first formulated the idea of the atom around the fifth century BCE. And just when we thought we understood its basic structure — protons, neutrons, and electrons — theories and technologies emerged to prove us wrong. Turns out, there are still more fundamental particles, like quarks, bound together by aptly named gluons.

Physicists discovered many of these and other particles in the enormous beasts of machines we call colliders, helping to develop what we know today as the Standard Model of physics. But there are questions that continue to nag: Is there something more fundamental still? Is the Standard Model all there is?

Determined to find out, the high energy physics community is working to integrate ever larger colliders and more sophisticated detectors with exascale computing systems. Among them is Walter Hopkins, an assistant physicist with Argonne National Laboratory and a collaborator with the ATLAS experiment at the Large Hadron Collider (LHC) at CERN, near Geneva, Switzerland.

Collaborating with researchers from both Argonne and Lawrence Berkeley National Lab, Hopkins leads an Aurora Early Science Program project through the ALCF to prepare software used in LHC simulations for exascale computing architectures, including Argonne’s forthcoming exascale machine, Aurora. At a billion billion calculations per second, Aurora is at the frontier of supercomputing and equal to the next challenge in particle physics, one of gargantuan magnitude.

The project was started several years ago by physicist and Argonne Distinguished Fellow James Proudfoot, who understood exascale’s distinct advantages in improving the impact of such complex science.

Aligning codes with new architecture

The collisions produced in the LHC occur in one of several detectors. The one on which the team is focused, ATLAS, witnesses billions of particle interactions every second and the signatures of new particles those collisions create in their wake.

One type of code the team is focused on, called event generators, simulates the underlying physics processes that occur at the interaction points within the 17-mile circumference collider ring. Getting the software-produced physics to align with that of the Standard Model helps researchers accurately simulate the collisions and predict the types, paths, and energies of the remnant particles.

Detecting physics in this way creates a mountain of data and requires an equally large chunk of computer time. And now, CERN is upping the ante as it readies to upgrade the LHC’s luminosity, allowing for more particle interactions and a 20-fold increase in data output.

While the team is looking to Aurora to handle this increase in their simulation requirements, the machine does not come without a few challenges of its own.

Workers inside ATLAS, one of several primary detectors for the Large Hadron Collider at CERN. ATLAS witnesses a billion particle interactions every second and the signatures of new particles created in near-speed-of-light proton-proton collisions. (Image: CERN)

Until recently, the event generators ran on computer CPUs (central processing units). While they work quickly, a CPU typically can only execute several operations at a time.

Aurora will be equipped with both CPUs and GPUs (graphic processing units), the choice of gamers everywhere. GPUs can handle many operations by breaking them into thousands of smaller tasks spread out across many cores, the engines that drive both types of unit.

But it takes a lot of effort to move CPU-based simulations onto GPUs in an efficient way, notes Hopkins. So, making this move to prepare for both Aurora and the onslaught of new data from LHC provides several challenges, which have become part of the team’s central focus.

“We want to be able to use Aurora to help us face these challenges,” says Hopkins, ​“but it requires us to study computing architectures that are new to us and our code base. For example, we’re focusing on a generator that is used in ATLAS, called MadGraph, and that runs on GPUs, which are more parallel and have different memory management requirements.”

A particle interaction simulation code, MadGraph was written by an international team of high energy physics theorists and supports the LHC’s simulation needs.

Simulation and AI support experimental work

The LHC has played a significant role in bringing prediction to reality. Most famously, the Standard Model predicted the existence of the Higgs boson, which conveys mass to all fundamental particles; ATLAS and its counterpart detector, CMS, confirmed Higgs’ existence in 2012.

But, as is so often the case in science, big discoveries can lead to more substantial questions, many of which are not predicted by the Standard Model. Why is the Higgs the mass that it is? What is dark matter?

“The reason for this very large upgrade to the LHC is that we’re hoping to find that needle in the haystack, that we’ll find some anomaly in the data set that offers a hint of physics beyond the Standard Model,” says Hopkins.

A combination of computational power, simulation, experiment, and artificial intelligence (AI) will dramatically help that search by providing accuracy in both prediction and identification.

When the ATLAS detector witnesses these particle collisions, for example, it records them as electronic signals. These are reconstructed as pixels of energy bursts that might correspond to an electron passing through.

“But just like in AI, where the canonical example is identifying cats and dogs in images, we have algorithms that identify and reconstruct those electronic signals into electrons, protons and other things,” says ALCF computer scientist Taylor Childers, a member of the team.

The reconstructed data from real collision events are then compared to the simulated data to look for differences in patterns. This is where accuracy in the physics models come to bear. If they’re working correctly and the real and simulated data doesn’t match, you continue to measure and rule out anomalies until it’s likely that you found that needle, that something that doesn’t fit the Standard Model.

The team is also using AI to quantify uncertainty, to determine the likelihood that they’ve identified a particle correctly.

Humans are capable of identifying particles to a limited extent — several parameters like momentum and position might tell us that a certain particle is an electron. But base that characterization on 10 parameters that are intimately tied together, then it’s another story, altogether.

“That’s where artificial intelligence really shines, especially if those input parameters are correlated, like the momentum of particles around an electron and the momentum of the electron itself,” says Hopkins. ​“These correlations are difficult to deal with analytically, but since we have so much simulation data, we can teach artificial intelligence and it can tell us, this is an electron with this likelihood because I have all of this input information.”

Exascale computing and the path forward

In advance of Aurora, the team continues work on the programming languages for the new architectures and the code to run on the Intel hardware that will be used on Aurora, as well as on hardware from other vendors.

“Part of the R&D that we do with our partner, Intel, is to make sure that the hardware is doing what we expect it to do and doing it efficiently,” says Childers. ​“Having a machine like Aurora will give us plenty of compute power and plenty of nodes to effectively reduce the time to solution, especially when we move to the upgraded LHC.”

The solution is an answer to a fundamental question — is there more beyond the Standard Model? — and one that could have unimagined repercussions a hundred years from now, notes Hopkins.

“Fundamental research can give us knowledge that may lead to societal transformation, but if we don’t do the research, it won’t lead to anything,” he says.

The ALCF is a DOE Office of Science User Facility.

Funding for this project was provided by DOE Office of Science: Offices of High Energy Physics and Advanced Scientific Computing Research. ATLAS is an international collaboration that benefits from DOE support.

About ALCF

The Argonne Leadership Computing Facility provides supercomputing capabilities to the scientific and engineering community to advance fundamental discovery and understanding in a broad range of disciplines. Supported by the U.S. Department of Energy’s (DOE’s) Office of Science, Advanced Scientific Computing Research (ASCR) program, the ALCF is one of two DOE Leadership Computing Facilities in the nation dedicated to open science.

Argonne National Laboratory seeks solutions to pressing national problems in science and technology. The nation’s first national laboratory, Argonne conducts leading-edge basic and applied scientific research in virtually every scientific discipline. Argonne researchers work closely with researchers from hundreds of companies, universities, and federal, state and municipal agencies to help them solve their specific problems, advance America’s scientific leadership and prepare the nation for a better future. With employees from more than 60 nations, Argonne is managed by UChicago Argonne, LLC for the U.S. Department of Energy’s Office of Science.

The U.S. Department of Energy’s Office of Science is the single largest supporter of basic research in the physical sciences in the United States and is working to address some of the most pressing challenges of our time. For more information, visit https://​ener​gy​.gov/​s​c​ience.

Click here to learn more.


Source: JOHN SPIZZIRRI, ALCF

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!

Empowering High-Performance Computing for Artificial Intelligence

April 19, 2024

Artificial intelligence (AI) presents some of the most challenging demands in information technology, especially concerning computing power and data movement. As a result of these challenges, high-performance computing Read more…

Kathy Yelick on Post-Exascale Challenges

April 18, 2024

With the exascale era underway, the HPC community is already turning its attention to zettascale computing, the next of the 1,000-fold performance leaps that have occurred about once a decade. With this in mind, the ISC Read more…

2024 Winter Classic: Texas Two Step

April 18, 2024

Texas Tech University. Their middle name is ‘tech’, so it’s no surprise that they’ve been fielding not one, but two teams in the last three Winter Classic cluster competitions. Their teams, dubbed Matador and Red Read more…

2024 Winter Classic: The Return of Team Fayetteville

April 18, 2024

Hailing from Fayetteville, NC, Fayetteville State University stayed under the radar in their first Winter Classic competition in 2022. Solid students for sure, but not a lot of HPC experience. All good. They didn’t Read more…

Software Specialist Horizon Quantum to Build First-of-a-Kind Hardware Testbed

April 18, 2024

Horizon Quantum Computing, a Singapore-based quantum software start-up, announced today it would build its own testbed of quantum computers, starting with use of Rigetti’s Novera 9-qubit QPU. The approach by a quantum Read more…

2024 Winter Classic: Meet Team Morehouse

April 17, 2024

Morehouse College? The university is well-known for their long list of illustrious graduates, the rigor of their academics, and the quality of the instruction. They were one of the first schools to sign up for the Winter Read more…

Kathy Yelick on Post-Exascale Challenges

April 18, 2024

With the exascale era underway, the HPC community is already turning its attention to zettascale computing, the next of the 1,000-fold performance leaps that ha Read more…

Software Specialist Horizon Quantum to Build First-of-a-Kind Hardware Testbed

April 18, 2024

Horizon Quantum Computing, a Singapore-based quantum software start-up, announced today it would build its own testbed of quantum computers, starting with use o Read more…

MLCommons Launches New AI Safety Benchmark Initiative

April 16, 2024

MLCommons, organizer of the popular MLPerf benchmarking exercises (training and inference), is starting a new effort to benchmark AI Safety, one of the most pre Read more…

Exciting Updates From Stanford HAI’s Seventh Annual AI Index Report

April 15, 2024

As the AI revolution marches on, it is vital to continually reassess how this technology is reshaping our world. To that end, researchers at Stanford’s Instit Read more…

Intel’s Vision Advantage: Chips Are Available Off-the-Shelf

April 11, 2024

The chip market is facing a crisis: chip development is now concentrated in the hands of the few. A confluence of events this week reminded us how few chips Read more…

The VC View: Quantonation’s Deep Dive into Funding Quantum Start-ups

April 11, 2024

Yesterday Quantonation — which promotes itself as a one-of-a-kind venture capital (VC) company specializing in quantum science and deep physics  — announce Read more…

Nvidia’s GTC Is the New Intel IDF

April 9, 2024

After many years, Nvidia's GPU Technology Conference (GTC) was back in person and has become the conference for those who care about semiconductors and AI. I Read more…

Google Announces Homegrown ARM-based CPUs 

April 9, 2024

Google sprang a surprise at the ongoing Google Next Cloud conference by introducing its own ARM-based CPU called Axion, which will be offered to customers in it 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…

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…

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…

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…

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…

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…

Leading Solution Providers

Contributors

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…

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…

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…

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…

Eyes on the Quantum Prize – D-Wave Says its Time is Now

January 30, 2024

Early quantum computing pioneer D-Wave again asserted – that at least for D-Wave – the commercial quantum era has begun. Speaking at its first in-person Ana Read more…

GenAI Having Major Impact on Data Culture, Survey Says

February 21, 2024

While 2023 was the year of GenAI, the adoption rates for GenAI did not match expectations. Most organizations are continuing to invest in GenAI but are yet to Read more…

The GenAI Datacenter Squeeze Is Here

February 1, 2024

The immediate effect of the GenAI GPU Squeeze was to reduce availability, either direct purchase or cloud access, increase cost, and push demand through the roof. A secondary issue has been developing over the last several years. Even though your organization secured several racks... Read more…

Intel’s Xeon General Manager Talks about Server Chips 

January 2, 2024

Intel is talking data-center growth and is done digging graves for its dead enterprise products, including GPUs, storage, and networking products, which fell to Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire