A modern twist on a World War II combat method is aiding the fight against the deadly Ebola virus — and could help avoid a catastrophic influenza pandemic.

Eric Jakobsson from the University of Illinois at Urbana-Champaign and the National Center for Supercomputing Applications, and Amir Barati Farimani from Stanford University, took their inspiration from the Blitz, and the randomly determined automatic firing patterns of anti-aircraft guns to protect London from German war planes. They’ve used similar stochastic algorithms, powered by GPUs, to develop multiple simulated molecular models that predict which antibodies would best combat certain strains of Ebola.

Keeping up with the evolution of Ebola, which mutates every three to four years, is difficult. Trying to outmaneuver influenza, which mutates every three to four months, is another story. Yet that’s just the challenge the research team intends to take on after Ebola.

“We know that if we continue on the path that we’re on, we will eventually have an influenza pandemic that will kill millions and millions of people. It’s inevitable,” said Jakobsson.

“The virus will eventually mutate in a way we can’t anticipate,” he said. “We’re hoping that the methods we’ve developed for Ebola can be applied to the flu to respond rapidly enough to a new super-virulent strain.”

Jakobsson and Farimani, a postdoctoral research fellow in chemistry at Stanford, set out to combat Ebola with a combination of history and technology. With the WWII history providing the strategy, the pair turned to the NCSA’s Blue Waters supercomputer, running NVIDIA Tesla K20X GPU accelerators, to run Farimani’s molecular simulations.

Normally, it would take 10 to 15 years and thousands of people to find an antibody to counter Ebola as medical researchers wait to see reactions in human test subjects.

By mimicking that process in simulations on Blue Waters, and applying bioinformatics and heavy data analytics, Jakobsson and Farimani were able to shorten the process of designing antibodies effective against Ebola.

The researchers ran hundreds of simulations, each of which required 24 to 48 hours of compute time. Ultimately, they predicted the movement of the modeled Ebola strains for about two years into the future.

“That creates a huge space for us to design the next generation of antibodies to counterattack that,” said Farimani.

Had the simulations run on CPUs, they would have taken up to 100 times as long, said Farimani. It’s that superior performance that has made a GPU-centric approach to solving the problem a no-brainer.

“That gives us the opportunity to try more and more,” said Jakobsson. “That’s the gift of GPUs.”

As the team works on getting ahead of influenza, the computing demands will ramp up, necessitating the use of machine learning and deep learning techniques, Jakobsson said.

“The great majority of flu strains cause only relatively minor illness and only a few people die, yet there is the potential for a really horrible pandemic,” said Jakobsson. “We want to help humankind be prepared to deal with that.”

Jakobsson and Farimani are preparing a research paper that will illustrate how their work demonstrates the promise of computation design of antibodies to reduce trial and error and thus enable quicker responses. When a truly virulent influenza strain arrives, they say, the ability to respond swiftly will be essential.

Feature image credit: NIAID

The post Mortal Combat: Getting Ahead of Flu Viruses with GPUs appeared first on The Official NVIDIA Blog.

So what do we think 2017 has in store for end-user computing? (here’s our recap of the foundation we built in 2016 — “Enterprise-Grade, Cloud-Ready”) Now let’s take a look forward in part two of this series, to see what the future will bring.

You’ll notice we didn’t say “GPU-accelerated,” because we’re thinking broader than faster graphics or app responsiveness. As our CEO Jen-Hsun Huang has shared, we’re entering a new era in computing, where GPUs are powering artificial intelligence and, more importantly, the new “AI enterprise” — one that uses GPU to gain faster insights and make decisions with superhuman power. In 2017 we’ll see this transformative effect first-hand, as the tip of the “GPU spear” makes its mark on virtual end-user computing, in the following ways:

The modern app and OS looks drastically different from its predecessor of five years. It’s twice as graphics-hungry (even if it’s not a “graphics”-app per se), and it was never coded with virtualization in mind. GPUs in the data center and cloud will answer this call, and enable every app, from simple email to browsers, to multimedia to office suites, all faithfully delivered with the physical device performance users take for granted. This really means two things: 1) the imaginary wall that exists between virtualizable and non-virtualizable apps will come down, and 2) to get there, you’ll find a GPU in every VDI host.

For a long time, organizations have been approaching the virtualization of workstations as mutually exclusive to retaining physical. No longer. We’re now seeing business embrace greater productivity gained when taking a portfolio approach to application delivery, and complementing professional graphics workstations with virtual ones. This will allow designers to move seamlessly from a physical environment where performance is maximized, to a virtual one where secure mobility is preferred, and back again. This efficient hybridization of workflow will unleash designer productivity like never before.

As we started, this year will be about the GPU-enabled enterprise. No longer will we be talking about one GPU for graphics virtualization or another GPU for accelerated computing. No more siloed infrastructure in the data center, or in the cloud. In the same way cloud enables the liquidity of workloads, the GPU-cloud will unify compute and graphics and enable enterprises to dynamically service both — tapping into the cloud for graphics-accelerated end-user computing, as well as analytics, deep learning and AI — all supported by a universal pool of GPU horsepower that intelligently multiplexes itself across all these demands.

We’re excited about what 2017 will bring, not for us, but for this new GPU-enabled enterprise that will demolish the barriers to productivity, improve security, redefine end-user computing economics and compete in the a global marketplace with previously unimaginable speed. For more information on how we’re transforming end-user computing through the power of the GPU, visit www.nvidia.com/grid.

The post Part 2: The State of the GPU-Powered Workspace in 2017 appeared first on The Official NVIDIA Blog.