SC17 Keynote – HPC Powers SKA Efforts to Peer Deep into the Cosmos

By John Russell

November 17, 2017

This week’s SC17 keynote – Life, the Universe and Computing: The Story of the SKA Telescope – was a powerful pitch for the potential of Big Science projects that also showcased the foundational role of high performance computing in modern science. It was also visually stunning as images of stars and galaxies and tiny telescopes and giant telescopes streamed across the high definition screen extended the length of Colorado Convention Center ballroom’s stage. One was reminded of astronomer Carl Sagan narrating the Cosmos TV series.

SKA, you may know, is the Square Kilometre Array project being run by an international consortium and intended to build the largest radio telescope in the world; it will be 50 times more powerful than any other radio telescope today. The largest today is  ALMA (Atacama Large Millimeter/submillimeter Array) located in Chile and has 66 dishes.

SKA will be sited in two locations, South Africa, and Australia. The two keynoters Philip Diamond, Director General of SKA, and Rosie Bolton, SKA Regional Centre Project Scientist and Project Scientist for the international engineering consortium designing the high performance computers, took turns outlining radio astronomy history and SKA’s ambition to build on that. Theirs was a swiftly-moving talk, both entertaining and informative. The visuals flashing adding to the impact.

Their core message: This massive new telescope will open a new window on astrophysical phenomena and create a mountain of data for scientists to work on for years. SKA, say Diamond and Bolton, will help clarify the early evolution of the universe, be able to detect gravitational waves by their effect on pulsars, shed light on dark matter, produce insight around cosmic magnetism, create detailed, accurate 3D maps of galaxies, and much more. It could even play a SETI like role in the search for extraterrestrial intelligence.

“When fully deployed, SKA will be able to detect TV signals, if they exist, from the nearest tens maybe 100 stars and will be able to detect the airport radars across the entire galaxy,” said Diamond, in response to a question. SKA is creating a new government organization to run the observatory, “something like CERN or the European Space Agency, and [we] are now very close to having this process finalized,” said Diamond.

Indeed this is exciting stuff. It is also incredibly computationally intensive. Think about an army of dish arrays and antennas, capturing signals 24×7, moving them over high speed networks to one of two digital “signal processing facilities”, one for each location, and then on to two “science data processors” centers (think big computers). And let’s not forget data must be made available to scientists around the world.

Consider just a few data points, shown below, that were flashed across stage during the keynote presentation. The context will become clearer later.

It’s a grand vision and there’s still a long way to go. SKA, like all Big Science projects, won’t happen overnight. SKA was first conceived in 90s at the International Union of Radio Science (URSI) which established the Large Telescope Working Group to begin a worldwide effort to develop the scientific goals and technical specifications for a next generation radio observatory. The idea arose to create a “hydrogen array” able to detect H radiofrequency emission (~1420 MHz). A square kilometer was required to have a large enough collection area to see back into the early universe. In 2011 those efforts consolidated in a not-for-profit company that now has ten member countries (link to brief history of SKA). The U.S. which did participate in early SKA efforts chose not to join the consortium at the time.

Although first conceived as a hydrogen array, Diamond emphasized, “With a telescope of that size you can study many things. Even in its early stages SKA will be able to map galaxies early in the universe’s evolution. When fully deployed it will conduct fullest galaxy mapping in 3D encompassing up to one million individual galaxies and cover 12.5 billon years of cosmic history.”

A two-phase deployment is planned. “We’re heading full steam towards critical design reviews next year,” said Diamond. Full construction starts in two years with construction of the first phase expected to begin in 2019. So far €200 million have been committed for design along with “a large fraction” of the €640 million required for first phase construction. Clearly there are technology and funding hurdles ahead. Diamond quipped if the U.S. were to join SKA and pony up, say $2 billion, they would ‘fix’ the spelling of kilometre to kilometer.

There will actually be two telescopes, one in South Africa about 600 km north of Cape Town and another one roughly 800 km north of Perth in western Australia. They are being located in remote regions to reduce radiofrequency interference from human activities.

“In South Africa we are going to be building close to 200 dishes, 15 meters in diameter, and the dishes will be spread over 150 km. They [will operate] over a frequency range of 350 MHz to 14 GHz. In Australia we will build 512 clusters, each of 256 antennas. That means a total of over 130,000 2-meter tall antennas, spread over 65 km. These low frequency antennas will be tapered with periodic dipoles and will cover the frequency range 50 to 350MHz. It is this array that will be the time machine that observes hydrogen all the way back to the dawn of the universe.”

Pretty cool stuff. Converting those signals into data is a mammoth task. SKA plans two different types of processing center for each location. “The radio waves induce voltages in the receivers that capture them and modern technology allows us to digitize them to higher precision than ever before. From there optical fibers transmit the digital data from the telescopes to what we call central processing facilities or (CPFs). There’s one for each telescope,” said Bolton.

Using a variety of technologies including “some exciting FPGA, CPU-GPU, and hybrids,” CPFs are where the signals are combined. Great care must be taken to first synchronize the data so it enters the processing chain exactly when it should to account for the fact the radio waves from space reached one antenna before reaching another. “We need to correct that phase offset down to the nanosecond,” said Bolton.

Once that’s done a Fourier transform is applied to the data. “It decomposes essentially a function of time into the frequencies that make it up; it moves us into the frequency domain. We do this with such precision that SKA will be able to process 65,000 different radio frequencies simultaneously,” said Diamond

Once the signals have been separated into frequencies they are processed one of two ways. “We can either stack the signals together of various antenna in what we call time domain data. Each stacking operation corresponds to a different direction in the sky. We’ll be able to look at 2000 such directions simultaneously. This time domain processing analysis detects repeating objects such as pulsars or one-off events like gamma ray explosions. If we do find an event, we are planning to store the raw voltage signals at the antennas for a few minutes so we can go back in time and investigate them to see what happened,” said Bolton.

This time domain data can be used by researchers to measure pulsar – which are a bit like cosmic lighthouses – signal arrival times accurately and detect the drift if there is one as a gravitational wave passes through.

“We can also use these radio signals to make images of the sky. To do that we take the signals from each pair of antennas, each baseline, and effectively multiply them together generating data objects we call visibilities. Imagine it will be done for 200 dishes and 512 groups of antennas, that’s 150,000 baselines ad 65,000 different frequencies. That makes up to 10 billion different data streams. Doing this is a data intensive process that requires around 50 petaflops of dedicated digital signal processing.

“Signals are processed inside these central processing facilities in a way that depends on the science that we want to do with them,” said Bolton. Once processed the data are then sent via more fiber optic cables to the Science Data Processors or SDPs. Two of these “great supercomputers” are planned, one in Cape Town for the dish array and one in Perth for low frequency antennas.

“We have two flavors of data within the Science Data Processors. In the time domain we’ll do panning for astrophysical gold, searching over 1.5M candidate objects every ten minutes sniffing out the real astrophysical phenomena such as pulsar signals or flashes of radio light,” said Diamond. The expectation is for a 10,000 to 1 negative-to-positive events. Machine learning will play a key role in finding the “gold.”

Making sense of the 10 billion incoming visibility data streams poses the greatest computational burden, emphasized Bolton: “This is really hard because inside the visibilities (data objects) the sky and antenna responses are all jumbled. We need to do another massive Fourier transform to get from the visibility space that depends on the antenna separations to sky planes. Ultimately we need to develop self-consistent models not only of the sky that generated the signals but also of how each antenna was behaving and even how the atmosphere was changing during the data gathering.

“We can’t do that in one fell swoop. Instead we’ll have several iterations trying to find the calibration parameters and source positions of brightnesses. With each iteration, bit by bit, fainter and fainter signal emerge from the noise. Every time we do another iteration we apply different calibration techniques and we improve a lot of them but we can’t be sure when this process is going to converge [on the best solution] so it is going to be difficult,” said Bolton.

A typical SKA map, she said, will probably contain hundreds of thousands of radio array sources. The incoming images are about 10 petabytes in size. Output 3D images are 5,000 pixels on each axis and 1 petabyte in size.

Distributing this data to scientists for analysis is another huge challenge. The plan is to distribute data via fiber to SKA regional centers. “This is another real game changer that the SKA, CERN, and a few other facilities are bringing about. Scientists will use the computing power of the SKA regional centers to analyze these data products,” said Diamond.

The keynote was a wowing, multimedia presentation, and warmly received by attendees. It bears repeating that many issues remain and schedules have slipped slightly, but it is still a stellar example of Big Science, requiring massively coordinated international efforts, and underpinned with enormous computing resources. Such collaboration is well aligned with SC17’s theme – HPC Connects.

Link to video recording of the presentation: https://www.youtube.com/watch?time_continue=2522&v=VceKNiRxDBc

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!

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…

Nvidia Appoints Andy Grant as EMEA Director of Supercomputing, Higher Education, and AI

March 22, 2024

Nvidia recently appointed Andy Grant as Director, Supercomputing, Higher Education, and AI for Europe, the Middle East, and Africa (EMEA). With over 25 years of high-performance computing (HPC) experience, Grant brings a 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…

Houston We Have a Solution: Addressing the HPC and Tech Talent Gap

March 15, 2024

Generations of Houstonian teachers, counselors, and parents have either worked in the aerospace industry or know people who do - the prospect of entering the fi 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