Astrophysical particle simulations with large custom GPU clusters on three continents
Computer Science - Research and Development
Speeding up large-scale geospatial polygon rasterization on GPGPUs
Proceedings of the ACM SIGSPATIAL Second International Workshop on High Performance and Distributed Geographic Information Systems
Teaching cross-platform design and testing methods for embedded systems using DICE
WESE '11 Proceedings of the 6th Workshop on Embedded Systems Education
Proceedings of the 43rd ACM technical symposium on Computer Science Education
Metasets: a new approach to partial membership
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
GPGPU implementation of growing neural gas: Application to 3D scene reconstruction
Journal of Parallel and Distributed Computing
PPAM'11 Proceedings of the 9th international conference on Parallel Processing and Applied Mathematics - Volume Part I
Optimized private information retrieval using graphics processing unit with reduced accessibility
Proceedings of the CUBE International Information Technology Conference
Accelerating the dynamic programming for the optimal polygon triangulation on the GPU
ICA3PP'12 Proceedings of the 12th international conference on Algorithms and Architectures for Parallel Processing - Volume Part I
An optimal parallel prefix-sums algorithm on the memory machine models for GPUs
ICA3PP'12 Proceedings of the 12th international conference on Algorithms and Architectures for Parallel Processing - Volume Part I
IDA'12 Proceedings of the 11th international conference on Advances in Intelligent Data Analysis
Design patterns for sparse-matrix computations on hybrid CPU/GPU platforms
Scientific Programming
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Graphics Processing Units (GPUs) are designed to be parallel - having hundreds of cores versus traditional CPUs. Increasingly, you can leverage GPU power for any computationally-intense operation - not just for graphics. If you're facing the challenge of programming systems to effectively use these massively parallel processors to achieve efficiency and performance goals, GPU Computing Gems provides a wealth of tested, proven GPU techniques. Learn from the leading researchers in concurrent programming, who have gathered their insights and experience in one volume under the guidance of NVIDIA and GPU expert Wen-mei Hwu. Covers the breadth of industry from scientific simulation and electronic design automation to audio / video processing, medical imaging, computer vision, and moreMany examples utilize NVIDIA's CUDA parallel computing architecture, the most widely-adopted GPU programming toolOffers insights and ideas as well as practical "hands-on" skills you can immediately put to use