Life Observed: Nobel Winner Sees Biology’s Future with GPUs
Released Nov. 26th, 2019

Five years ago, when Eric Betzig got the call he won a Nobel Prize for inventing a microscope that could see features as small as 20 nanometers, he was already working on a new one.

The new device captures the equivalent of 3D video of living cells — and now it’s using NVIDIA GPUs and software to see the results.

Betzig’s collaborator at the University of California at Berkeley, Srigokul Upadhyayula (aka Gokul), helped refine the so-called Lattice Light Sheet system. It generated 600 terabytes of data while exploring part of the visual cortex of a mouse in work published earlier this year in Science magazine. A 1.3TB slice of that effort was on display at NVIDIA’s booth at last week’s SC19 supercomputing show.

Attendees got a glimpse of how tomorrow’s scientists may unravel medical mysteries. Researchers, for example, can use Lattice to watch how protein coverings on nerve axons degrade as diseases such as muscular sclerosis take hold.

Future of Biology: Direct Visualization

“It’s our belief we will never understand complex living systems by breaking them into parts,” Betzig said of methods such as biochemistry and genomics. “Only optical microscopes can look at living systems and gather information we need to truly understand the dynamics of life, the mobility of cells and tissues, how cancer cells migrate. These are things we can now directly observe.

“The future of biology is direct visualization of living things rather than piecing together information gleaned by very indirect means,” he added.

It Takes a Cluster — and More

Such work comes with heavy computing demands. Generating the 600TB dataset for the Science paper “monopolized our institution’s computing cluster for days and weeks,” said Betzig.

“These microscopes produce beautifully rich data we often cannot visualize because the vast majority of it sits in hard drives, completely useless,” he said. “With NVIDIA, we are finding ways to start looking at it.”

The SC19 demo — a multi-channel visualization of a preserved slice of mouse cortex — ran remotely on six NVIDIA DGX-1 servers, each packing eight NVIDIA V100 Tensor Core GPUs. The systems are part of an NVIDIA SATURNV cluster located near its headquarters in Santa Clara, Calif.

Berkeley researchers gave SC19 attendees a look inside the visual cortex of a mouse — visualized using NVIDIA IndeX.

The key ingredient for the demo and future visualizations is NVIDIA IndeX software, an SDK that allows scientists and researchers to see and interact in real time with massive 3D datasets.

Version 2.1 of IndeX debuted at SC19, sporting a host of new features, including GPUDirect Storage, as well as support for Arm and IBM POWER9 processors.

After seeing their first demos of what IndeX can do, the research team installed it on a cluster at UC Berkeley that uses a dozen NVIDIA TITAN RTX and four V100 Tensor Core GPUs. “We could see this had incredible potential,” Gokul said.

Closing a Big-Data Gap

The horizon holds plenty of mountains to climb. The Lattice scope generates as much as 3TB of data an hour, so visualizations are still often done on data that must be laboriously pre-processed and saved offline.

“In a perfect world, we’d have all the information for analysis as we get the data from the scope, not a month or six months later,” said Gokul. The time between collecting and visualizing data can stretch from weeks to months, but “we need to tune parameters to react to data as we’re collecting it” to make the scope truly useful for biologists, he added.

NVIDIA IndeX software, running on its increasingly powerful GPUs, helps narrow that gap.

In the future, the team aims to apply the latest deep learning techniques, but this too presents heady challenges. “There are no robust AI models to deploy for this work today,” Gokul said.

Making the data available to AI specialists who could craft AI models would require shipping crates of hard drives on an airplane, a slow and expensive proposition. That’s because the most recent work produced over half a petabyte of data, but cloud services often limit uploads and downloads to a terabyte or so per day.

Betzig and Gokul are talking with researchers at cloud giants about new options, and they’re exploring new ways to leverage the power of GPUs because the potential of their work is so great.

Coping with Ups and Downs

“Humans are visual animals,” said Betzig. “When most people I know think about a hypothesis, they create mental visual models.

“The beautiful thing about microscopy is you can take a model in your head with all its biases and immediately compare it to the reality of living biological images. This capability already has and will continue to reveal surprises,” he said.

The work brings big ups and downs. Winning a Nobel Prize “was a shock,” Betzig said. “It kind of felt like getting hit by a bus. You feel like your life is settled and then something happens to change you in ways you wouldn’t expect — it has good and bad sides to it.”

Likewise, “in the last several years working with Gokul, every microscope had its limits that led us to the next one. You take five or six steps up to a plateau of success and then there is a disappointment,” he said.

In the partnership with NVIDIA, “we get to learn what we may have missed,” he added. “It’s a chance for us to reassess things, to understand the GPU from folks who designed the architecture, to see how we can merge our problem sets with new solutions,” he said.

Note: The picture at top shows Berkeley researchers Eric Betzig, Ruixian Gao and Srigokul Upadhyayula with the Lattice Light Sheet microscope.

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