high performance computing on graphics processing units: hgpu.org

Programming

hgpu.org » Programming

Data Compression using CUDA programming in GPU

Ganapathy Mani

Tags: Algorithms, Compression, Computer science, CUDA, Data parallelism, nVidia, nVidia GeForce 8800 GTS

December 8, 2012 by hgpu

Smoothed Particle Hydrodynamics Simulation for Continuous Casting

Adithya Vijaykumar

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Tags: Fluid dynamics, nVidia, nVidia GeForce GTX 580, OpenCL, Solidification, Thesis

December 8, 2012 by hgpu

A Computationally Efficient Parallel Kernel Regression for Image Reconstruction

V.Sairam, M.Srinivasa Rao, G.Dada Khalandhar, L.Srikanth, P.K.Baruah, R.R.Sarma

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Tags: Algorithms, CUDA, Image processing, Image reconstruction, Medicine, nVidia, Tesla T20, Tomography

December 8, 2012 by hgpu

A computationally efficient and scalable approach for privacy preserving kNN classification

Sairam Ravu, P. R. Neelakandan, M. R. Gorai, R. Mukkamala, P. K. Baruah

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Tags: Algorithms, Bloom filter, Computer science, CUDA, Data mining, nVidia, Tesla M2070

December 8, 2012 by hgpu

GPGPU-Aided 3D Staggered-grid Finite-difference Seismic Wave Modeling

Chang Cai, Haiqing Chen, Ze Deng, Dan Chen, Samee U. Khan, Ke Zeng, Minxiao Wu

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Tags: CUDA, Earth and Space Sciences, Finite difference, nVidia, nVidia GeForce GTX 680, Seismic modeling, Seismology

December 8, 2012 by hgpu

GPU accelerating the FEniCS Project

Fredrik Heffer Valdmanis

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Tags: Computer science, CUDA, Differential equations, Linear Algebra, nVidia, Partial differential equations, PDEs, Tesla C2070, Thesis

December 8, 2012 by hgpu

Lightweight Modular Staging and Embedded Compilers: Abstraction Without Regret for High-Level High-Performance Programming

Tiark Rompf

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Tags: Code generation, Compilers, Computer science, CUDA, Heterogeneous systems, nVidia, Performance, Software Engineering, Tesla C2050, Thesis

December 8, 2012 by hgpu

A Data-Parallel Graphics Pipeline Implemented in OpenCL

Joel Ek

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Tags: Computer science, Filtering, OpenCL, Rasterization, Rendering, Thesis

December 5, 2012 by hgpu

Mapping Streaming Applications to OpenCL

Abhishek Ray

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Tags: ATI, ATI Radeon HD 6970, Computer science, nVidia, OpenCL, Programming techniques, Tesla C2050

December 5, 2012 by hgpu

Parallel Cosegmentation via Submodular Optimization on Anisotropic Diffusion

Dinesh Majeti, Aditya Prakash, S. Balasubramanian, PK Baruah

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Tags: Clustering, CUDA, CULA, Image processing, nVidia, Optimization, Tesla S2050

December 5, 2012 by hgpu

Gauge Field Generation on Large-Scale GPU-Enabled Systems

Frank Winter

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Tags: Computational Physics, CUDA, High Energy Physics - Lattice, nVidia, Physics, QCD, Tesla M2090

December 5, 2012 by hgpu

Usage of GPU in LS-DYNA

Uli Goehner

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Tags: CAD, Computer science, CUDA, nVidia, Tesla M2050

December 5, 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

See all packages

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high performance computing on graphics processing units: hgpu.org

Programming

Data Compression using CUDA programming in GPU

Smoothed Particle Hydrodynamics Simulation for Continuous Casting

A Computationally Efficient Parallel Kernel Regression for Image Reconstruction

A computationally efficient and scalable approach for privacy preserving kNN classification

GPGPU-Aided 3D Staggered-grid Finite-difference Seismic Wave Modeling

GPU accelerating the FEniCS Project

Lightweight Modular Staging and Embedded Compilers: Abstraction Without Regret for High-Level High-Performance Programming

A Data-Parallel Graphics Pipeline Implemented in OpenCL

Mapping Streaming Applications to OpenCL

Parallel Cosegmentation via Submodular Optimization on Anisotropic Diffusion

Gauge Field Generation on Large-Scale GPU-Enabled Systems

Usage of GPU in LS-DYNA

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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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