Intel Technology Journal - Compute-Intensive, Highly Parallel Applications and Uses

	Intel® Technology Journal Home

	Volume 09, Issue 02

	Compute-Intensive, Highly Parallel Applications and Uses

	Table of Contents

	Technical Reviewers

	About This Journal

	Intel Published Articles

	Read Past Journals

	Subscribe

	E-Mail this Journal to a Collegue


	Technology and Research Intel Technology Journal

Compute-Intensive, Highly Parallel Applications and Uses

Volume 09 Issue 02 Published May 19, 2005
ISSN 1535-864X DOI: 10.1535/itj.0902

Foreword

	Recognition, Mining and Synthesis
	By: Bob Liang Director, Application Research Lab, Corporate Technology Group Pradeep Dubey Senior Principal Engineer and Manager, Innovative Platform Architecture, Corporate Technology Group

Current Articles

Graphics

	Ray Tracing Goes Mainstream
	Ray Tracing puts us on the path to true photo-realistic rendering. Join us in comparing and contrasting ray tracing with the raster equivalent, and analyze its platform requirements and scalability potential.

Computer Vision

	Computer Vision Workload Analysis: Case Study of Video Surveillance Systems
	Using video surveillance as a representative case study, investigate with us how computer vision applications scale to future parallel computing platforms that enable its use in mass-market applications.

	Learning-Based Computer Vision with Intel’s Open Source Computer Vision Library
	Explore some of the Open Source Computer Vision Library's (OpenCV) learning routines for rigid object finding (faces) and for flexible object segmentation (roads).

Data Mining

	Performance Scalability of Data-Mining Workloads in Bioinformatics
	Understand how algorithms used in bioinformatics are representative of emerging data-mining workloads, and analyze the performance scalability on parallel architectures by characterizing the behavior of the cache memory hierarchy.

	Performance and Scalability Analysis of Tree-Based Models in Large-Scale Data-Mining Problems
	Modern learning techniques incur high computational loads. Building such models in online interactive mode is a challenging task for upcoming platforms. Consider with us several data-mining methods based on ensembles of trees.

Large-Scale Optimizations

	Parallel Computing for Large-Scale Optimization Problems: Challenges and Solutions
	The Interior Point Method (IPM) is used for solving large optimization problems for scientific, engineering, and commercial applications. Learn how coarse-grain and fine-grain parallelism can be exploited, and analyze the performance scalability on shared memory parallel architectures.

Platform Requirements

	Understanding the Platform Requirements of Emerging Enterprise Solutions
	Learn how Intel architects and technologists must comprehend and influence changes in enterprise data center usage and deployment models as they develop platform solutions aligned to emerging end-user needs.

Preface


	Lin Chao Publisher Intel Technology Journal “If a man can write a better book, preach a better sermon, or make a better mouse trap than his neighbor, though he build his house in the woods, the world will make a beaten path to his door.” The sentence, usually shortened to emphasize the better mouse trap, is by Ralph Waldo Emerson (1803-1882), an American poet and philosopher. What Emerson said so long ago applies even today as inventive people work to build a better computer mouse such as wireless or optical. In the Research and Development Labs at Intel, we are applying Emerson’s philosophy to building smarter computers which are more natural and easier to use—a lot less strict and a lot more adaptive to humans. more...