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GPU Computing

main_pp-RES-NT2NVIDIA® TeslaTM M-class GPUs are massively parallel companion processors to CPUs that deliver unprecedented performance on a range of signal processing, image analysis and video forensic applications. Coupled with off-the-shelf developer toolkits for C, C++, and Fortran and powerful signal and image processing libraries, such as GPU-VSIPL and NPP (NVIDIA Performance Primitives), Themis’s Tesla GPU-accelerated servers offer a robust solution for a range of HPC applications outside the traditional data center environment.

Why GPU Computing? Defense and Intelligence Applications

With the ever-increasing demand for more computing performance, the HPC industry is moving to a hybrid computing model, where GPUs and CPUs work together to perform general purpose computing tasks. As parallel processors, GPUs excel at tackling large amounts of similar data because the problem can be split into hundreds or thousands of pieces and calculated simultaneously. As sequential processors, CPUs are not designed for this type of computation, but they are adept at more serial-based tasks such as running operating systems and organizing data. NVIDIA’s GPU solutions outpace others as they apply the most relevant processor to the specific task in hand.

GPU Computing increases application performance and improves performance per watt. With the goal of processing more of the data at the source of collection, GPUs allow supercomputing performance in a much smaller footprint and offer the best value for many IT budgets. Current defense and intelligence applications include:

  • Geospatial Imaging
  • Video Analytics
  • Aerodynamics/CFD
  • Computer Vision
  • Signal Processing
  • Electromagnetics

GPUS are Revolutionizing Computing

The high performance computing (HPC) industry’s need for computation is increasing, as large and complex computational problems become commonplace across many industry segments. Traditional CPU technology, however, is no longer capable of scaling in performance sufficiently to address this demand.

The parallel processing capability of the Graphics Processing Unit (GPU) allows it to divide complex computing tasks into thousands of smaller tasks that can be run concurrently. This ability is enabling computational scientists and researchers to address some of the world’s most challenging computational problems up to several orders of magnitude faster.

The use of GPUs for computation is a dramatic shift in HPC. GPUs deliver performance increases of 10x to 100x to solve problems in minutes instead of hours, outpacing the performance of traditional computing with x86-based CPUs alone. In addition, GPUs also deliver greater performance per watt of power consumed. From climate modeling to medical tomography, NVIDIA® Tesla™ GPUs are enabling a wide variety of segments in science and industry to progress in ways that were previously impractical, or even impossible, due to technological limitations.[/DDET]