ORNL Summit Supercomputer Is Officially Here

By Tiffany Trader

June 8, 2018

Oak Ridge National Laboratory (ORNL) together with IBM and Nvidia celebrated the official unveiling of the Department of Energy (DOE) Summit supercomputer today at an event presided over by DOE Secretary Rick Perry. The partners, who collaborated to design and build the estimated $200-million dollar machine under the CORAL procurement program, heralded it as the world’s most powerful supercomputer with 200 peak petaflops for high-performance computing workloads and 3.3 peak exaops for emerging AI workloads.

The deployment encompasses 4,608 compute nodes, each containing two 22-core IBM Power9 processors and six Nvidia Tesla V100 GPUs, interconnected with dual-rail Mellanox EDR 100Gb/s InfiniBand. Summit is said to offer 8X more performance than its predecessor, Titan, which spans 18,688 AMD-Nvidia nodes. The new supercomputer has a power footprint of 13MW, not a significant increase over Titan’s 9MW considering the massive performance leap. Summit will include a 250PB IBM Spectrum Scale file system. This parallel file system, named Alpine

DOE Secretary Rick Perry at Summit unveiling

Perry upheld Summit’s installation as a sign of the United States’ global competitiveness and technological leadership:

“We know we’re in a competition and we know that this competition is real and it matters who gets there first,” said Perry. “Today [we] show the rest of the world that America is back in the game and we’re back in the game in a big way. Our national security, our economics, our scientific discovery, our energy research will be affected in a powerful way.”

Perry warned however that the U.S. also faces a challenge. “There are other nations that are racing to develop their technology; if we’re not dedicated and determined, the leadership we enjoy today could be the leadership of tomorrow and we don’t want that,” he said.

While this soft-launch (formal acceptance is scheduled for later this year) is an important milestone that is generating wide media attention, the HPC community proper is still awaiting and expects hard benchmarks; they won’t have to wait too much longer with the next Top500 list due out in two weeks. If Summit achieves the Linpack score that we’ve heard projected, roughly 120-petaflops, the United States could retake the Top500 crown from China, pending no surprises. China has held the top of the list since 2013 with the debut of the 33.9-petaflops (Linpack) Tianhe-2A. That machine fell to number two in 2016, when China stood up the 93-petaflops (Linpack) Sunway TaihuLight, which still holds the number one spot. The fastest U.S. machine is still the Oak Ridge Titan supercomputer, which entered the list at the number one position in November 2012 (with 17.6 Linpack petaflops) and now ranks fifth.

Perry emphasized the importance of supercomputing leadership to the United States’ administration, stating, “President Trump is determined to make America first in supercomputing.” He referenced the President’s March budget, noting it includes $677 million in funding for exascale activities, and indicated further funding increases are likely. (See our latest exascale budget coverage here.) The procurement process for Summit’s successor, named Frontier, is already underway. The plan is for the CORAL-2 machine to be the nation’s first capable exascale supercomputer with delivery timed for the second half of 2021.

The Linpack metric that the Top500 listing is based on, though imperfect, is a more meaningful way to rank machines than peak capability. Of course, the only benchmark that really matters is how a supercomputer performs on real applications. At the unveiling today, ORNL Director Thomas Zacharia noted that one of the earliest science applications carried out on Summit broke the mixed-precision exascale barrier.

Each Summit node uses six Nvidia Volta GPUs per two Power9 CPUs, tied together with Nvidia’s NVLink 2.0 technology (Image credit: Jason Richards/ORNL)

During early testing, researchers at Oak Ridge achieved 1.88 exaops using Summit’s V100 GPU Tensor cores to run a comparative genomics code that analyzes variation between human genome sequences. The run was carried out using a representative dataset on 4,000 nodes, achieving a computational efficiency of greater than 50 percent. Summit enabled a 25-fold speedup for the code compared to the lab’s previous leadership-class supercomputer Titan with the Tensor cores alone providing a 4.5-fold application speedup. (See ORNL’s writeup for more details.)

Summit, according to Oak Ridge and its partners, is poised to provide unprecedented computing power and deep learning capability to enable scientific discoveries that were previously impractical or impossible, and will advance research in energy, advanced materials and artificial intelligence (AI) and other domains. Its power will also be lent to improving the care of military veterans through a partnership with the US Department of Veterans Affairs that began in 2016.

Some of the science projects slated to run on Summit (as described by Oak Ridge):

Astrophysics

Exploding stars, known as supernovas, supply researchers with clues related to how heavy elements—including the gold in jewelry and iron in blood—seeded the universe.

The highly scalable FLASH code models this process at multiple scales—from the nuclear level to the large-scale hydrodynamics of a star’s final moments. On Summit, FLASH will go much further than previously possible, simulating supernova scenarios several thousand times longer and tracking about 12 times more elements than past projects.

“It’s at least a hundred times more computation than we’ve been able to do on earlier machines,” said ORNL computational astrophysicist Bronson Messer. “The sheer size of Summit will allow us to make very high-resolution models.”

Materials

Developing the next generation of materials, including compounds for energy storage, conversion and production, depends on subatomic understanding of material behavior. QMCPACK, a quantum Monte Carlo application, simulates these interactions using first-principles calculations.

Up to now, researchers have only been able to simulate tens of atoms because of QMCPACK’s high computational cost. Summit, however, can support materials composed of hundreds of atoms, a jump that aids the search for a more practical superconductor—a material that can transmit electricity with no energy loss.

“Summit’s large, on-node memory is very important for increasing the range of complexity in materials and physical phenomena,” said ORNL staff scientist Paul Kent. “Additionally, the much more powerful nodes are really going to help us extend the range of our simulations.”

Cancer Surveillance

One of the keys to combating cancer is developing tools that can automatically extract, analyze and sort existing health data to reveal previously hidden relationships between disease factors such as genes, biological markers and environment. Paired with unstructured data such as text-based reports and medical images, machine learning algorithms scaled on Summit will help supply medical researchers with a comprehensive view of the U.S. cancer population at a level of detail typically obtained only for clinical trial patients.

This cancer surveillance project is part of the CANcer Distributed Learning Environment, or CANDLE, a joint initiative between DOE and the National Cancer Institute.

“Essentially, we are training computers to read documents and abstract information using large volumes of data,” ORNL researcher Gina Tourassi said. “Summit enables us to explore much more complex models in a time efficient way so we can identify the ones that are most effective.”

Systems Biology

Applying machine learning and AI to genetic and biomedical datasets offers the potential toaccelerate understanding of human health and disease outcomes.

Using a mix of AI techniques on Summit, researchers will be able to identify patterns in the function, cooperation and evolution of human proteins and cellular systems. These patterns can collectively give rise to clinical phenotypes, observable traits of diseases such as Alzheimer’s, heart disease or addiction, and inform the drug discovery process.

Through a strategic partnership project between ORNL and the U.S. Department of Veterans Affairs, researchers are combining clinical and genomic data with machine learning and Summit’s advanced architecture to understand the genetic factors that contribute to conditions such as opioid addiction.

“The complexity of humans as a biological system is incredible,” said ORNL computational biologist Dan Jacobson. “Summit is enabling a whole new range of science that was simply not possible before it arrived.”

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