HPCwire: Computed Tomography Software Taps Into NVIDIA GPUs

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November 26, 2008

Computed Tomography Software Taps Into NVIDIA GPUs

by John West, for HPCwire

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The growing adoption of high performance computing on small scale clusters by companies from all segments of the economy is driven by the same forces that led HPC to become an integral part of the fabric of science and engineering decades earlier: HPC helps users get things done they just couldn't do before.

In science and engineering that has meant test flying new aircraft designs years before a prototype could have been built in a traditional physical testing workflow, or rationally searching for new therapeutic compounds based on specific desirable biological properties rather than hit and miss experimentation. These advances have created real gains in the standard of living for most of the people on this planet. This is scientific computing in the large, and the impacts are culture shifting. But although the scale of the computation might be smaller, the shift that adoption of today's HPC technologies is causing for businesses and their customers is nonetheless transformative.

I recently talked with Julien Noel, the CT (computed tomography) Product Manager at North Star Imaging in chilly Rogers, Minnesota. North Star Imaging (NSI) specializes in industrial X-ray for nondestructive testing and analysis. They have seen firsthand how the adoption of HPC -- in the form of expanded computational power from NVIDIA's GPUs and their CUDA API -- can transform a business and create new value for them and for their customers.

HPCwire: What does North Star do for its customers?

Noel: Our 2D digital X-ray systems are often used throughout the manufacturing process for product quality control and manual or automated approval/rejection applications. Our 3D CT systems have typically been used for research and development, failure analysis, reverse engineering and other similar tasks.

Our products and services are geared toward anyone who needs to inspect an object internally and/or externally without destroying it. We are involved with industries such as aerospace, medical device, electronics, automotive, museums and many more, and have had the opportunity to work with companies such as Boeing, Bell Helicopter, Lockheed Martin, NASA, US Army, Medtronic, Kawasaki and the list goes on.

HPCwire: What is the problem you are solving with HPC -- in this case NVIDIA's GPUs?

Noel: Computed tomography involves complex algorithms for 3D reconstruction. Basically, the industrial CT system takes several 2D digital X-ray images and reconstructs them into a 3D volume made of voxels or volume elements. This process uses a filtered back-projection algorithm called the Feldkamp Algorithm.

Due to the improvement of digital X-ray technology, industrial CT systems are able to take more X-ray projections than ever before --from 720 to 3,000-- plus each individual image is becoming appreciably larger. Single images now reach 3 to 10 megapixels and have a bit depth usually around 14 to 16 bits. Overall, CT software manipulates massive datasets, as well as creates and outputs multi-billion voxel-sized reconstructions.

In order to process the data and create the reconstruction, the CT software requires a high-end computer with significant computation capability. To date, the standard has been either a multi-core processor system or a computer farm, which in turn leads to expensive hardware and a limit in reconstruction speed. Basically, the CT reconstruction speed is linear with the number of processors -- that is, 8 cores equals 8 times faster.

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