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INTENSIVE
2009
IEEE
15 years 4 months ago
Accelerating K-Means on the Graphics Processor via CUDA
In this paper an optimized k-means implementation on the graphics processing unit (GPU) is presented. NVIDIA’s Compute Unified Device Architecture (CUDA), available from the G8...
Mario Zechner, Michael Granitzer
97
Voted
SIMPRA
2011
14 years 4 months ago
A real-time multigrid finite hexahedra method for elasticity simulation using CUDA
In this paper we present a GPU-based multigrid approach for simulating elastic deformable objects in real time. Our method is based on a finite element discretization of the defo...
Christian Dick, Joachim Georgii, Rüdiger West...
IPPS
2010
IEEE
14 years 7 months ago
An auto-tuning framework for parallel multicore stencil computations
Although stencil auto-tuning has shown tremendous potential in effectively utilizing architectural resources, it has hitherto been limited to single kernel instantiations; in addi...
Shoaib Kamil, Cy Chan, Leonid Oliker, John Shalf, ...
JPDC
2008
167views more  JPDC 2008»
14 years 9 months ago
A performance study of general-purpose applications on graphics processors using CUDA
Graphics processors (GPUs) provide a vast number of simple, data-parallel, deeply multithreaded cores and high memory bandwidths. GPU architectures are becoming increasingly progr...
Shuai Che, Michael Boyer, Jiayuan Meng, David Tarj...
66
Voted
ICRA
2009
IEEE
204views Robotics» more  ICRA 2009»
15 years 4 months ago
A high-speed multi-GPU implementation of bottom-up attention using CUDA
— In this paper a novel implementation of the saliency map model on a multi-GPU platform using CUDA technology is presented. The saliency map model is a wellknown computational m...
Tingting Xu, Thomas Pototschnig, Kolja Kühnle...