Using AI and Supercomputers, Researchers Remove One of the Biggest Roadblocks in Astrophysics

May 6, 2021

May 6, 2021 — Using a bit of machine learning magic, astrophysicists can now simulate vast, complex universes in a thousandth of the time it takes with conventional methods. The new approach will help usher in a new era in high-resolution cosmological simulations, its creators report in a study published on May 4, 2021 in the Proceedings of the National Academy of Sciences.

“At the moment, constraints on computation time usually mean we cannot simulate the universe at both high resolution and large volume,” says study lead author Yin Li, an astrophysicist at the Flatiron Institute in New York City. “With our new technique, it’s possible to have both efficiently. In the future, these AI-based methods will become the norm for certain applications.”

The new method developed by Li and his colleagues feeds a machine learning algorithm with models of a small region of space at both low and high resolutions. The algorithm learns how to upscale the low-res models to match the detail found in the high-res versions. Once trained, the code can take full-scale low-res models and generate ‘super-resolution’ simulations containing up to 512 times as many particles.

The process is akin to taking a blurry photograph and adding the missing details back in, making it sharp and clear.

Simulations of a region of space 100 million light-years square. The leftmost simulation ran at low resolution. Using machine learning, researchers upscaled the low-res model to create a high-resolution simulation (right). That simulation captures the same details as a conventional high-res model (middle) while requiring significantly fewer computational resources. [Credit: Y. Li et al./Proceedings of the National Academy of Sciences 2021]
Li is a recipient of the Leadership Resource Allocation (LRAC) on the Frontera supercomputer for the project, “Super resolution cosmological simulations of galaxies and quasars” (PI: Tiziana Di Matteo). Frontera, at the Texas Advanced Computing Center (TACC), is the fastest academic supercomputer in the world and the team is one of the largest users of this massive computing resources, which is funded by the U.S. National Science Foundation and began operations in 2019. To date, they have used more than 2.2 million node hours (each node contains 56 processing cores) on Frontera.

“In cosmological simulations, the dynamic range quickly makes this physically complex problem intractable,” said Tiziana Di Matteo of Carnegie Mellon University, a co-author on the paper. “Our new way forward is through the development of methods that leverage the AI revolution using Frontera resources, making “super-resolution” simulations possible.

Di Matteo, Croft and Ni are part of Carnegie Mellon’s National Science Foundation (NSF) Planning Institute for Artificial Intelligence in Physics, which supported this work, and members of Carnegie Mellon’s McWilliams Center for Cosmology.

The team’s hybrid approach involves combining petascale-plus hydrodynamic simulations with Neural Networks and other ML algorithms.

“Our goal is to create models of the entire observable Universe that incorporate information from higher resolution models of individual galaxies,” Di Matteo continued. “Frontera is ideal for this: allowing us to couple the physics and AI running on GPUs and CPUs, and enable us to reach detail which would be otherwise impossible.”

The upscaling brings significant savings in compute time. For a region in the universe roughly 500 million light-years across containing 134 million particles, existing methods would require 560 hours to churn out a high-res simulation using a single processing core. With the new approach, the researchers need only 36 minutes.

3D visualization of low-, high-, and super-resolution (LR, HR, and SR) dark matter density and velocity fields at z = 0. The top two rows show the LR and HR simulations, which share the same seed for initial conditions but are 512 times different in mass resolution. The bottom panels show the SR realization generated by our trained model. [Credit: Y. Li et al./Proceedings of the National Academy of Sciences 2021]
The results were even more dramatic when more particles were added to the simulation. For a universe 1,000 times as large with 134 billion particles, the researchers’ new method took 16 hours on a single graphics processing unit. Existing methods would take so long that they wouldn’t even be worth running without dedicated supercomputing resources, Li says.

Li is a joint research fellow at the Flatiron Institute’s Center for Computational Astrophysics and the Center for Computational Mathematics. He co-authored the study with Yueying Ni, Rupert Croft and Tiziana Di Matteo of Carnegie Mellon University; Simeon Bird of the University of California, Riverside; and Yu Feng of the University of California, Berkeley.

Cosmological simulations are indispensable for astrophysics. Scientists use the simulations to predict how the universe would look in various scenarios, such as if the dark energy pulling the universe apart varied over time. Telescope observations may then confirm whether the simulations’ predictions match reality. Creating testable predictions requires running simulations thousands of times, so faster modeling would be a big boon for the field.

Reducing the time it takes to run cosmological simulations “holds the potential of providing major advances in numerical cosmology and astrophysics,” says Di Matteo. “Cosmological simulations follow the history and fate of the universe, all the way to the formation of all galaxies and their black holes.”

So far, the new simulations only consider dark matter and the force of gravity. While this may seem like an oversimplification, gravity is by far the universe’s dominant force at large scales, and dark matter makes up 85 percent of all the ‘stuff’ in the cosmos. The particles in the simulation aren’t literal dark matter particles but are instead used as trackers to show how bits of dark matter move through the universe.

The team’s code used neural networks to predict how gravity would move dark matter around over time. Such networks ingest training data and run calculations using the information. The results are then compared to the expected outcome. With further training, the networks adapt and become more accurate.

The specific approach used by the researchers, called a generative adversarial network, pits two neural networks against each other. One network takes low-resolution simulations of the universe and uses them to generate high-resolution models. The other network tries to tell those simulations apart from ones made by conventional methods. Over time, both neural networks get better and better until, ultimately, the simulation generator wins out and creates fast simulations that look just like the slow conventional ones.

“We couldn’t get it to work for two years,” Li says, “and suddenly it started working. We got beautiful results that matched what we expected. We even did some blind tests ourselves, and most of us couldn’t tell which one was ‘real’ and which one was ‘fake.'”

Despite only being trained using small areas of space, the neural networks accurately replicated the large-scale structures that only appear in enormous simulations.

The simulations don’t capture everything, though. Because they focus only on dark matter and gravity, smaller-scale phenomena — such as star formation, supernovae and the effects of black holes — are left out. The researchers plan to extend their methods to include the forces responsible for such phenomena, and to run their neural networks ‘on the fly’ alongside conventional simulations to improve accuracy. “We don’t know exactly how to do that yet, but we’re making progress,” Li says.

Read a companion analysis by the group on arXiv.org


Source: TACC

Subscribe to HPCwire's Weekly Update!

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

Q&A with Google’s Bill Magro, an HPCwire Person to Watch in 2021

June 11, 2021

Last Fall Bill Magro joined Google as CTO of HPC, a newly created position, after two decades at Intel, where he was responsible for the company's HPC strategy. This interview was conducted by email at the beginning of A Read more…

A Carbon Crisis Looms Over Supercomputing. How Do We Stop It?

June 11, 2021

Supercomputing is extraordinarily power-hungry, with many of the top systems measuring their peak demand in the megawatts due to powerful processors and their correspondingly powerful cooling systems. As a result, these Read more…

Honeywell Quantum and Cambridge Quantum Plan to Merge; More to Follow?

June 10, 2021

Earlier this week, Honeywell announced plans to merge its quantum computing business, Honeywell Quantum Solutions (HQS), which focuses on trapped ion hardware, with the U.K.-based Cambridge Quantum Computing (CQC), which Read more…

ISC21 Keynoter Xiaoxiang Zhu to Deliver a Bird’s-Eye View of a Changing World

June 10, 2021

ISC High Performance 2021 – once again virtual due to the ongoing pandemic – is swiftly approaching. In contrast to last year’s conference, which canceled its in-person component with a couple months’ notice, ISC Read more…

Xilinx Expands Versal Chip Family With 7 New Versal AI Edge Chips

June 10, 2021

FPGA chip vendor Xilinx has been busy over the last several years cranking out its Versal AI Core, Versal Premium and Versal Prime chip families to fill customer compute needs in the cloud, datacenters, networks and more. Now Xilinx is expanding its reach to the booming edge... Read more…

AWS Solution Channel

Building highly-available HPC infrastructure on AWS

Reminder: You can learn a lot from AWS HPC engineers by subscribing to the HPC Tech Short YouTube channel, and following the AWS HPC Blog channel. Read more…

Space Weather Prediction Gets a Supercomputing Boost

June 9, 2021

Solar winds are a hot topic in the HPC world right now, with supercomputer-powered research spanning from the Princeton Plasma Physics Laboratory (which used Oak Ridge’s Titan system) to University College London (which used resources from the DiRAC HPC facility). One of the larger... Read more…

A Carbon Crisis Looms Over Supercomputing. How Do We Stop It?

June 11, 2021

Supercomputing is extraordinarily power-hungry, with many of the top systems measuring their peak demand in the megawatts due to powerful processors and their c Read more…

Honeywell Quantum and Cambridge Quantum Plan to Merge; More to Follow?

June 10, 2021

Earlier this week, Honeywell announced plans to merge its quantum computing business, Honeywell Quantum Solutions (HQS), which focuses on trapped ion hardware, Read more…

ISC21 Keynoter Xiaoxiang Zhu to Deliver a Bird’s-Eye View of a Changing World

June 10, 2021

ISC High Performance 2021 – once again virtual due to the ongoing pandemic – is swiftly approaching. In contrast to last year’s conference, which canceled Read more…

Xilinx Expands Versal Chip Family With 7 New Versal AI Edge Chips

June 10, 2021

FPGA chip vendor Xilinx has been busy over the last several years cranking out its Versal AI Core, Versal Premium and Versal Prime chip families to fill customer compute needs in the cloud, datacenters, networks and more. Now Xilinx is expanding its reach to the booming edge... Read more…

What is Thermodynamic Computing and Could It Become Important?

June 3, 2021

What, exactly, is thermodynamic computing? (Yes, we know everything obeys thermodynamic laws.) A trio of researchers from Microsoft, UC San Diego, and Georgia Tech have written an interesting viewpoint in the June issue... Read more…

AMD Introduces 3D Chiplets, Demos Vertical Cache on Zen 3 CPUs

June 2, 2021

At Computex 2021, held virtually this week, AMD showcased a new 3D chiplet architecture that will be used for future high-performance computing products set to Read more…

Nvidia Expands Its Certified Server Models, Unveils DGX SuperPod Subscriptions

June 2, 2021

Nvidia is busy this week at the virtual Computex 2021 Taipei technology show, announcing an expansion of its nascent Nvidia-certified server program, a range of Read more…

Using HPC Cloud, Researchers Investigate the COVID-19 Lab Leak Hypothesis

May 27, 2021

At the end of 2019, strange pneumonia cases started cropping up in Wuhan, China. As Wuhan (then China, then the world) scrambled to contain what would, of cours Read more…

AMD Chipmaker TSMC to Use AMD Chips for Chipmaking

May 8, 2021

TSMC has tapped AMD to support its major manufacturing and R&D workloads. AMD will provide its Epyc Rome 7702P CPUs – with 64 cores operating at a base cl Read more…

Intel Launches 10nm ‘Ice Lake’ Datacenter CPU with Up to 40 Cores

April 6, 2021

The wait is over. Today Intel officially launched its 10nm datacenter CPU, the third-generation Intel Xeon Scalable processor, codenamed Ice Lake. With up to 40 Read more…

Berkeley Lab Debuts Perlmutter, World’s Fastest AI Supercomputer

May 27, 2021

A ribbon-cutting ceremony held virtually at Berkeley Lab's National Energy Research Scientific Computing Center (NERSC) today marked the official launch of Perlmutter – aka NERSC-9 – the GPU-accelerated supercomputer built by HPE in partnership with Nvidia and AMD. Read more…

CERN Is Betting Big on Exascale

April 1, 2021

The European Organization for Nuclear Research (CERN) involves 23 countries, 15,000 researchers, billions of dollars a year, and the biggest machine in the worl Read more…

Google Launches TPU v4 AI Chips

May 20, 2021

Google CEO Sundar Pichai spoke for only one minute and 42 seconds about the company’s latest TPU v4 Tensor Processing Units during his keynote at the Google I Read more…

Iran Gains HPC Capabilities with Launch of ‘Simorgh’ Supercomputer

May 18, 2021

Iran is said to be developing domestic supercomputing technology to advance the processing of scientific, economic, political and military data, and to strengthen the nation’s position in the age of AI and big data. On Sunday, Iran unveiled the Simorgh supercomputer, which will deliver.... Read more…

HPE Launches Storage Line Loaded with IBM’s Spectrum Scale File System

April 6, 2021

HPE today launched a new family of storage solutions bundled with IBM’s Spectrum Scale Erasure Code Edition parallel file system (description below) and featu Read more…

Quantum Computer Start-up IonQ Plans IPO via SPAC

March 8, 2021

IonQ, a Maryland-based quantum computing start-up working with ion trap technology, plans to go public via a Special Purpose Acquisition Company (SPAC) merger a Read more…

Leading Solution Providers

Contributors

10nm, 7nm, 5nm…. Should the Chip Nanometer Metric Be Replaced?

June 1, 2020

The biggest cool factor in server chips is the nanometer. AMD beating Intel to a CPU built on a 7nm process node* – with 5nm and 3nm on the way – has been i Read more…

Julia Update: Adoption Keeps Climbing; Is It a Python Challenger?

January 13, 2021

The rapid adoption of Julia, the open source, high level programing language with roots at MIT, shows no sign of slowing according to data from Julialang.org. I Read more…

AMD Launches Epyc ‘Milan’ with 19 SKUs for HPC, Enterprise and Hyperscale

March 15, 2021

At a virtual launch event held today (Monday), AMD revealed its third-generation Epyc “Milan” CPU lineup: a set of 19 SKUs -- including the flagship 64-core, 280-watt 7763 part --  aimed at HPC, enterprise and cloud workloads. Notably, the third-gen Epyc Milan chips achieve 19 percent... Read more…

Can Deep Learning Replace Numerical Weather Prediction?

March 3, 2021

Numerical weather prediction (NWP) is a mainstay of supercomputing. Some of the first applications of the first supercomputers dealt with climate modeling, and Read more…

Livermore’s El Capitan Supercomputer to Debut HPE ‘Rabbit’ Near Node Local Storage

February 18, 2021

A near node local storage innovation called Rabbit factored heavily into Lawrence Livermore National Laboratory’s decision to select Cray’s proposal for its CORAL-2 machine, the lab’s first exascale-class supercomputer, El Capitan. Details of this new storage technology were revealed... Read more…

GTC21: Nvidia Launches cuQuantum; Dips a Toe in Quantum Computing

April 13, 2021

Yesterday Nvidia officially dipped a toe into quantum computing with the launch of cuQuantum SDK, a development platform for simulating quantum circuits on GPU-accelerated systems. As Nvidia CEO Jensen Huang emphasized in his keynote, Nvidia doesn’t plan to build... Read more…

Microsoft to Provide World’s Most Powerful Weather & Climate Supercomputer for UK’s Met Office

April 22, 2021

More than 14 months ago, the UK government announced plans to invest £1.2 billion ($1.56 billion) into weather and climate supercomputing, including procuremen Read more…

African Supercomputing Center Inaugurates ‘Toubkal,’ Most Powerful Supercomputer on the Continent

February 25, 2021

Historically, Africa hasn’t exactly been synonymous with supercomputing. There are only a handful of supercomputers on the continent, with few ranking on the Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire