The NVIDIA Blog

Zapping Cost of Cancer Treatment Using Laser-Driven Ion Accelerators and GPU Computing

Posted: 03 Apr 2015 10:24 AM PDT

Radiation therapies with ion beams can precisely target cancerous tumors, while leaving surrounding healthy tissue unharmed.

Such targeted therapy leads to less invasive surgery, shorter hospital stays and speedier recovery times.

The drawback is that conventional ion accelerators tend to be huge in both size and cost. This puts them beyond the budget for most medical facilities.

Researchers are using powerful GPU clusters to develop new code and simulations, with the ultimate goal of creating high-powered, laser-driven ion accelerators that are compact and less expensive.

Michael Bussmann, who leads a research group in computational radiation physics at Helmholtz-Zentrum Dresden-Rossendorf in Dresden, Germany, is driving these efforts with GPU computing.

Creating live simulations and adding physical phenomena to code was once considered impossible. Now, with the parallel processing power of GPUs, it's achievable.

When assessing electrons, each individual particle can be followed and its contribution to the overall radiation emitted from laser-driven plasma can be calculated, Bussman said.

To bring laser acceleration of ions to applications requires realistic simulations. PIConGPU code, a school project that turned into a research lab-run project, is one way to get it done.

Operating on a cluster of GPUs, the particle-in-cell on a GPU, or PIConGPU, has become one of the most widely used algorithms in computational plasma physics because it opens up a new understanding of laser-matter interactions.

Using GPUs also offers more computational power, faster processing times and cuts the time needed for coding. A simulation that once took a week can now be completed in a matter of hours.

New high-power lasers are being built with PIConGPU, including the petawatt-laser Penelope, currently under construction at Helmholtz-Zentrum Dresden-Rossendorf.

"We'll have codes to better estimate how the laser will work, because with treatments, it's never just one shot," Bussmann said. "We want to find more ways to treat cancer. There are many therapies, and lasers mean you can provide treatment from all different points."

Eventually, researchers hope to use Penelope to estimate the exact dose of treatment given to a patient.

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Yes, GPUs Can Save You From Malware

Posted: 02 Apr 2015 11:37 AM PDT

It's a staggering challenge.

The proliferation of malware — malicious software that often targets the mountain of data on computers and mobile devices — poses a huge problem for the information security world, complicated by its taking on new forms and techniques.

Increasingly, firms like Avast Software are addressing the problem with GPUs.

The Czech security vendor has built a GPU-accelerated database that lets it process and analyze millions of samples dramatically faster than traditional tools, Peter Kovac, an Avast senior researcher told attendees during a session at the GPU Technology Conference.

"It allows us to do nearest-neighbor queries, rule-matching queries and classification of unknown records," Kovac said of the database, dubbed Medusa.

Each day, Avast's sensor network monitors hundreds of millions of user machines, and identifies hundreds of thousands of potential new malware files. When the network collects what it believes is a suspicious file, it sends it to the company's submit servers, which pass it on to Medusa. There, the GPUs do their magic, applying queries and classifying files based on the results.

Medusa then stores records of those files to compare against future suspected malware. The database contains nearly 40 million clean files, nearly 3 million recent threats and about 2 million samples for which it hasn't made a determination.

Kovac said there are "billions of billions" of possible combinations to analyze, which requires a stochastic approach. Because of the huge, random nature of the possibilities, Medusa comes up with a typical representative of a cluster that uses 60 to 80 percent of its attributes. The goal is to find a workable subset of conditions so Avast can maximize its success in identifying malware in real time.

Without GPUs, none of this would be possible because of the enormous amount of data in question.

"GPUs do the heavy lifting," Kovac said.

How heavy? How about rule-matching queries happening 22X faster than on a CPU, rule generation that's 20X faster and nearest-neighbor queries that happen 13X faster?

The results speak for themselves.

"Hundreds of results from the rule generator are released daily," said Kovac. In other words, millions of users' computers are spared.

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