SCIENCE

Data computation has been called the "third pillar of science," alongside the ancient pillars of logic and observation, and upon which future scientific breakthroughs will rest.  Morgan Kaufmann, a global leader in cutting-edge computing content, today announced the availability of the first volume in a new book series that will showcase how advanced parallel computing techniques are revolutionizing scientific research.  

The GPU Computing Gems series was developed in collaboration with NVIDIA, the world leader in visual and parallel computing.  Graphics processing units (GPUs), which were invented by NVIDIA, have revolutionized data computation and are playing a key role in enabling leading researchers and academics to drive the next wave of scientific discovery.  The GPU Computing Gems series will provide practical techniques and real-world examples straight from the leading minds in GPU-based research, demonstrating how new parallel computing techniques can be harnessed within different domains to enable new scientific breakthroughs.

GPU Computing Gems: Emerald Edition is the first volume in the series, and is available today from key book retailers and e-tailers.  Visit http://mkp.com/gpu-computing-gems for more information.

Computational scientists increasingly utilize GPUs for computational-intensive applications to achieve dramatic improvements in processing power, efficiency and power consumption.  The challenge for developers in the new arena of scientific research is learning how to program systems that effectively use these concurrent processors to achieve these goals, and GPU Computing Gems was created to provide real-world tips and guidance to assist researchers.  Each chapter presents techniques used in leading research, designed to be accessible to others in multiple fields and disciplines, allowing knowledge to cross-pollinate across the GPU spectrum.

GPU Computing Gems: Emerald Edition will focus on how GPU computing can be applied to:

• Scientific Simulation
• Life Sciences
• Statistical Modeling
• Emerging Data-Intensive Applications
• Electronic Design Automation
• Ray Tracing and Rendering
• Computer Vision
• Video and Image Processing
• Signal and Audio Processing
• Medical Imaging