Georgia Tech researchers are helping the military to unlock the number-crunching potential of graphics processor cards.
It has long been known that common-or-garden graphics processors (GPUs), found in every PC, make incredibly fast vector processors - but they are tough to programme.
"As radar systems and other sensor systems get more complicated, the computational requirements are becoming a bottleneck," said Georgia Tech engineer Daniel Campbell. "We are capitalising on the ability of GPUs to process radar, infrared sensor and video data faster than a typical computer and at a much lower cost and power than a computing cluster."
The university is easing programming by writing functions defined in the Vector, Signal and Image Processing Library (VSIPL) to run on GPUs.
VSIPL is an open standard developed by embedded signal and image processing hardware and software vendors, academia, application developers and government labs.
GPU VSIPL is available here.
Currently they are writing in Nvidia's CUDA language but, according to Campbell, the underlying principles can be applied to GPUs developed by other companies.
With GPU VSIPL, engineers can use high-level functions in C to perform linear algebra and signal processing operations, and recompile with GPU VSIPL to take advantage of GPU speed.
The researcher claim that, depending on the function and size of the data set, VSIPL functions operate between 20 and 350 times faster on a GPU than a CPU, .
"The results are not surprising because GPUs excel at performing repetitive arithmetic tasks like those in VSIPL, such as signal processing functions like Fourier transforms, spectral analysis, image formation and noise filtering," said research engineer Mark Richards. "We've just alleviated the need for engineers to understand the entire GPU architecture by simply providing them with a library of routines that they frequently use."
US Department of defence has signal and image processing benchmarks.
"Preliminary studies have shown several of the benchmarks have straightforward parallelisation schemes that result in faster operation without requiring significant optimisation," said Georgia Tech. "For other benchmarks, additional research needs to be conducted into optimising the use of multiple GPUs."
Plans are afoot to develop additional defence-related GPU function libraries, and design programming tools for other processors, such as Cell, found in the PlayStation 3.
Mark Richards (left), Daniel Campbell (centre) and Andrew Kerr are using PC graphics cards to perform linear algebra and signal processing.
Mark Richards (left), Andrew Kerr (centre) and Daniel Campbell have shown that VSIPL functions operate 20-350 times faster on a PC graphics card than a CPU, depending on the function and size of the data set.
Georgia Tech researchers have reprogrammed the Vector, Signal and Image Processing Library (VSIPL) to run on graphics processing units (GPUs) like this.