high performance computing on graphics processing units: hgpu.org

Programming

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On the Correctness of the SIMT Execution Model of GPUs

Axel Habermaier, Alexander Knapp

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Tags: Computer science, CUDA, nVidia, Task scheduling, Thesis

January 20, 2012 by hgpu

Experiences in Teaching a Specialty Multicore Computing Course

Peter E. Strazdins

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Tags: Cloud, Computer science, CUDA, Education, nVidia, Package, Virtualization

January 20, 2012 by hgpu

Stochastic Progressive Photon Mapping for Dynamic Scenes

Maayan Weiss, Thorsten Grosch

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Tags: 3D Graphics and Realism, Algorithms, Image processing, nVidia, nVidia GeForce GTX 285

January 20, 2012 by hgpu

Parallel Volume Rendering for Large Scientific Data

Thomas Fogal

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Tags: Algorithms, Computer science, MRI, nVidia, nVidia GeForce 8800 GTX, nVidia Quadro FX 5800, OpenGL, Rendering, Visualization

January 20, 2012 by hgpu

Leveraging on High-Performance Computing and Cloud Technologies in Digital Libraries: A Case Study

Peter Wittek, Sandor Daranyi

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Tags: Algorithms, Cloud, Computer science, Machine learning, MapReduce, Text mining

January 20, 2012 by hgpu

Efficient fMRI Analysis and Clustering on GPUs

B.E. Dharneesh Talasu

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Tags: Clustering, CUDA, Heterogeneous systems, Hierarchical clustering, Image processing, Medicine, MRI, nVidia, nVidia GeForce 9400 M, Thesis

January 20, 2012 by hgpu

Coherent Spatiotemporal Filtering, Upsampling and Rendering of RGBZ Videos

Christian Richardt, Carsten Stoll, Neil A. Dodgson, Hans-Peter Seidel, Christian Theobalt

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Tags: 3D Graphics and Realism, Algorithms, Computer science, Filtering, Image processing, Rendering

January 20, 2012 by hgpu

Dynamically Managed Data for CPU-GPU Architectures

Thomas B. Jablin, James A. Jablin, Prakash Prabhu, Feng Liu, David I. August

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Tags: Computer science, CUDA, Data Structures and Algorithms, Memory model, nVidia, nVidia GeForce GTX 480, Performance

January 19, 2012 by hgpu

Combining approximate inference methods for efficient learning on large computer clusters

Zhenwen Dai, Jacquelyn A. Shelton, Jorg Bornschein, Abdul Saboor Sheikh, Jorg Lucke

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Tags: Algorithms, Computational Complexity, Computer science, GPU cluster, Machine learning, MPI, nVidia, nVidia GeForce GTX 480, OpenCL, Python

January 19, 2012 by hgpu

Accelerated Large-Scale Multiple Sequence Alignment

Scott Lloyd, Quinn O. Snell

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Tags: Algorithms, Bioinformatics, Biology, FPGA, Package, Sequence alignment

January 19, 2012 by hgpu

Platform Characterization for Domain-Specific Computing

Alex Bui, Kwang-Ting (Tim) Cheng, Jason Cong, Luminita Vese, Yi-Chu Wang, Bo Yuan, Yi Zou

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Tags: Computer science, CUDA, FPGA, Heterogeneous systems, nVidia, Tesla C1060

January 19, 2012 by hgpu

Human Re-identification System On Highly Parallel GPU and CPU Architectures

Slawomir Bak, Krzysztof Kurowski, Krystyna Napierala

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Tags: Algorithms, Computer science, Computer vision, CUDA, nVidia, Tesla S1070

January 19, 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

Programming

On the Correctness of the SIMT Execution Model of GPUs

Stochastic Progressive Photon Mapping for Dynamic Scenes

Parallel Volume Rendering for Large Scientific Data

Efficient fMRI Analysis and Clustering on GPUs

Coherent Spatiotemporal Filtering, Upsampling and Rendering of RGBZ Videos

Dynamically Managed Data for CPU-GPU Architectures

Combining approximate inference methods for efficient learning on large computer clusters

Accelerated Large-Scale Multiple Sequence Alignment

Platform Characterization for Domain-Specific Computing

Human Re-identification System On Highly Parallel GPU and CPU Architectures

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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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