high performance computing on graphics processing units: hgpu.org

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Approximate Similarity Search for Online Multimedia Services on Distributed CPU-GPU Platforms

George Teodoro, Eduardo Valle, Nathan Mariano, Ricardo Torres, Wagner Meira Jr, Joel H. Saltz

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Tags: Algorithms, Computer science, CUDA, Databases, Nearest neighbour, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 470

September 4, 2012 by hgpu

Energy Transfer Ray Tracing with OptiX

Scot Halverson

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, OpenGL, Raytracing

September 3, 2012 by hgpu

Accelerated Flow Visualization of Advective-Diffusive Mixing Processes Using GPUs

Alejandro Aguilar Sierra, Luis M. de la Cruz Salas

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia Quadro FX 3800 M, OpenGL, Tesla C1060, Visualization

September 3, 2012 by hgpu

Mixed-Resolution Patch-Matching

Harshit Sureka, P. J. Narayanan

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Tags: Algorithms, Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 580

September 3, 2012 by hgpu

GPU-accelerated WZ Factorization with the Use of the CUBLAS Library

Beata Bylina, Jaroslaw Bylina

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Tags: Computer science, CUBLAS, CUDA, Factorization, nVidia, Tesla C2050

September 3, 2012 by hgpu

Solving Systems of Polynomial Equations on a GPU

Robert A. Klopotek, Joanna Porter-Sobieraj

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 460

September 3, 2012 by hgpu

Performance Analysis on Several GPU Architectures of an Algorithm for Noise Removal

M. G. Sanchez, V. Vidal, J. Bataller, G. Verdu

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Tags: Algorithms, CUDA, Image processing, nVidia, nVidia GeForce 9800 GX2, nVidia GeForce GT 120, Signal denoising, Tesla M2050

September 1, 2012 by hgpu

GPU Acceleration of Genetic Algorithms for Subset Selection for Partial Fault Tolerance

D. Foster

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Tags: Computer science, CUDA, Fault tolerance, Genetic programming, nVidia, nVidia GeForce GTX 480

September 1, 2012 by hgpu

Towards large-scale network analytics

Xintian Yang

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Tags: Algorithms, Compression, Computer science, CUDA, Heterogeneous systems, nVidia, Tesla C1060, Thesis, Visualization

September 1, 2012 by hgpu

Parallel GPU-accelerated Recursion-based Generators of Pseudorandom Numbers

Przemyslaw Stpiczynski, Dominik Szalkowski, Joanna Potiopa

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Tags: Algorithms, Computer science, CUDA, nVidia, OpenCL, Tesla M2050

September 1, 2012 by hgpu

A GPU Support for Large Scale Quantum Chemistry Applications

Selva Kumar Sengottaiyan, Fang Liu, Masha Sosonkina

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Tags: Chemistry, CUDA, nVidia, Tesla C2070, Visualization

September 1, 2012 by hgpu

GAROP: Genetic Algorithm framework for Running On Parallel environments

Tomoyuki Hiroyasu, Ryosuke Yamanaka, Masato Yoshimi, Mitsunori Miki

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Tags: Algorithms, Computer science, CUDA, Genetic programming, nVidia, Tesla C2050

September 1, 2012 by hgpu

Tutoring LLM into a Better CUDA Optimizer

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Kernel Library for LLM Serving

Compiler and Runtime Systems for Generative AI Models

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

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high performance computing on graphics processing units: hgpu.org

Approximate Similarity Search for Online Multimedia Services on Distributed CPU-GPU Platforms

Energy Transfer Ray Tracing with OptiX

Accelerated Flow Visualization of Advective-Diffusive Mixing Processes Using GPUs

Mixed-Resolution Patch-Matching

GPU-accelerated WZ Factorization with the Use of the CUBLAS Library

Solving Systems of Polynomial Equations on a GPU

Performance Analysis on Several GPU Architectures of an Algorithm for Noise Removal

GPU Acceleration of Genetic Algorithms for Subset Selection for Partial Fault Tolerance

Towards large-scale network analytics

Parallel GPU-accelerated Recursion-based Generators of Pseudorandom Numbers

A GPU Support for Large Scale Quantum Chemistry Applications

GAROP: Genetic Algorithm framework for Running On Parallel environments

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Tutoring LLM into a Better CUDA Optimizer

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

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