NVIDIA's Tesla GPU compute accelerators used in the calculations

May 30, 2013 11:37 GMT  ·  By

GPU compute acceleration has scored quite a few points in the field of research, especially when it comes to computations in need of a lot of parallel processing. Math never really did go as quickly as it could have on x86 chips.

That is why the University of Illinois at Urbana-Champaign (UIUC) researchers are so happy to have access to NVIDIA Tesla L20X GPU compute accelerators.

Which is to say, they used the Blue Waters Supercomputer, powered by 3,000 NVIDIA Tesla K20X GPU accelerators, in their research efforts.

And said efforts were aimed at trying to find some way to deal with the HIV virus.

So far, a cure has eluded the world, and this situation will probably persist for some time still.

Yet progress is being made. The UIHC researchers uncovered detail about the capsid structure of the virus.

They determined the precise chemical structure of the HIV "capsid," a protein shell that protects the virus's genetic material.

The shell is key to the virulence of HIV. Cracking it could spell the end of the disease.

"GPUs help researchers push the envelope of scientific discovery, enabling them to solve bigger problems and gain insight into larger and more complex systems," said Sumit Gupta, general manager of the Tesla accelerated computing business unit at NVIDIA.

"Blue Waters and the Titan supercomputer, the world's No. 1 open science supercomputer at Oak Ridge National Labs, are just two of many GPU-equipped systems that are enabling the next wave of real-world scientific discovery."

So far, HIV has killed 25 million people and infected 34 million more. Although, if we were to be technical, it wasn't HIV that killed them. HIV only kills white blood cells, leaving bearers vulnerable to even the weakest diseases.

"It would have been very difficult to run a simulation of this size without the power of GPU-accelerated supercomputing in the Blue Waters system," said Klaus Schulten, professor of physics at the University of Illinois.

"We started using GPU accelerators more than five years ago, and GPUs have fundamentally accelerated the pace of our research."