IBM's 'Cell University Challenge' Winners Uncover Breakthrough Applications for Brain Monitoring, Data Mapping, Medical Imaging and Object Detection

Market Wire, September, 2007

Today at the 2007 Power Architecture Developer Conference (PADC), IBM (NYSE: IBM) announced the winners of its first annual Cell Broadband Engine(TM) (Cell/B.E.) Processor University Challenge. From the thousands of innovative entries, winning designs featured never-before-seen uses of the Cell/B.E. technology, including large-scale modeling of the human brain; a system for mapping massive amounts of real-time data; a path to deliver complex, 3-D medical images to a desktop computer; and a new way to detect extremely fast-moving objects.

Nearly 80,000 students from 25 countries competed in the Challenge, which consisted of online trivia about Cell/B.E. -- originally designed by IBM, Sony Group and Toshiba Corp., for use in consumer devices such as Sony Computer Entertainment's PLAYSTATION©3 -- followed by an opportunity to invent their own applications using this powerful processor. Students' designs included everything from applications-oriented solutions (e.g., visualization, medical imaging, seismic computing, etc.) to High Performance Computing (HPC) to industry-wide programmability tools.

"This contest provided a growth opportunity for students to gain real-life, multi-disciplinary skills to apply to their futures as they move from the classroom to the workforce," said Nick Donofrio, IBM executive vice president, Innovation and Technology. "This challenge also proved the true power, potential and promise of student innovations."

The teams with winning designs were each presented a cash prize ranging from $2,500 to $10,000 for their work. These included:

--  First Place -- Cluster of Sony PlayStation3's used for large-scale
    modeling of the human brain. Using the same technology that runs in today's
    video games, students Jayram Moorkanikara Nageswaran and Jeff Furlong from
    the University of California, Irvine (USA), and Ashok Chandrashekar and
    Andrew Felch from the Neukom Institute for Computational Science at
    Dartmouth College (USA), created a low-cost cluster able to support the
    complex algorithms used in brain research. This study addressed issues of
    known difficulty in visual processing; for example, using standard
    processors, the complex computations needed to emulate the human brain's
    ability to rapidly and effortlessly recognize objects, was found to be slow
    and inefficient. By exploiting Cell/B.E.'s parallel instruction set and
    extending it into low-cost clusters using Sony PS3s, the students were able
    to show a 100x performance boost over smaller clusters.

--  Second Place -- A new path developed for mapping large-scale data.
    MapReduce for Cell/B.E. is a simple and flexible parallel programming
    model, initially proposed by Google, for large-scale data processing in a
    distributed computing environment. This implementation for Cell/B.E.
    enabled programmers to easily use the resources of a large distributed
    system. In a performance evaluation at the University of Wisconsin, Madison
    (USA), student Marc de Kruijf used synthetic benchmarks representative of a
    diverse application space. For computationally intensive applications, he
    showed in excess of a 2.5x performance improvement over a 2.4GHz Intel
    Core2 processor, with linear scaling as more Synergistic Processing
    Elements (SPEs) were added. The runtime overhead was also minimal, at less
    than 4 percent. This was the first application of its kind for Cell/B.E.

--  Third Place -- Complex 3-D imaging brought from devices, such as MRIs,
    to the desktop. The importance of volume rendering has been increased as
    the amount of data grows due to widespread use of 3-D imaging devices such
    as Computed Tomography (CT), 3-D laser scanners and Magnetic Resonance
    Imaging (MRI) equipment. The technique, called ray-casting, recognized as
    one of the best for image quality, has been limited to a set amount of data
    due to its slowness. The recent Cell/B.E. architecture provided
    opportunities to finally put the ray-casting into the practical use at the
    desktop computers of scientists and engineers. Jusub Kim from University of
    Maryland at College Park (USA) presented a new volume ray-casting algorithm
    designed to fully take advantage of Cell/B.E benefits and showed Cell/B.E
    is the main enabling technology in providing the-finest-image-quality
    volume rendering on practical data size. Experimental results showed one
    can interactively render 256x256x256 data onto a 256x256 image at $\sim$15
    frames/sec with one Cell/B.E processor, which was about 100 times faster
    than the same implementation at Intel Xeon 3GHz.

--  Fourth Place -- A new way developed to detect fast-moving objects. A
    project by students Robert Hiramatsu and Jussara Kofuji at the University
    of São Paulo (Brazil) re-implemented rapid object detection on an Open
    Computer Visual library (OpenCV) and used efficient ways to process on the
    SPEs of CELL/B.E. OpenCV has direct relevance to cutting-edge visualization
    applications such as facial recognition. In the team's implementation, they
    used a specific approach of classifiers that restricted use of an image
    reference of 24 x 24 pixels and worked with a stump-based classifier
    algorithm to reduce data structure for classifiers.