high performance computing on graphics processing units: hgpu.org

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Efficient Support for Matrix Computations on Heterogeneous Multi-core and Multi-GPU Architectures

Fengguang Song, Stanimire Tomov, Jack Dongarra

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Tags: Algorithms, BLAS, Computer science, CUDA, Factorization, Heterogeneous systems, nVidia, Tesla M2070

November 20, 2011 by hgpu

Optimizing Symmetric Dense Matrix-Vector Multiplication on GPUs

Rajib Nath, Stanimire Tomov, Tingxing "Tim" Dong, Jack Dongarra

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Tags: Algorithms, Computer science, CUBLAS, CUDA, Linear Algebra, nVidia, nVidia GeForce GTX 280, Package, Performance, Tesla C2050

November 20, 2011 by hgpu

Using the High Productivity Language Chapel to Target GPGPU Architectures

Albert Sidelnik, Maria J. Garzaran, David Padua, Bradford L. Chamberlain

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Tags: Compilers, Computer science, CUDA, High-level Languages, nVidia, nVidia GeForce GTX 280, Optimization

November 19, 2011 by hgpu

Anisotropic mesh coarsening and refinement on GPU architecture

Matthew Potter

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Tags: Algorithms, CUDA, Differential equations, Finite volume method, Fluid dynamics, nVidia, Partial differential equations, PDEs, Tesla M2050, Thesis

November 19, 2011 by hgpu

Exploiting concurrent kernel execution on graphic processing units

Lingyuan Wang, Miaoqing Huang, Tarek El-Ghazawi

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Tags: Computer science, CUDA, nVidia, Performance, Programming techniques, Tesla C2070

November 19, 2011 by hgpu

Towards Efficient GPU Sharing on Multicore Processors

Lingyuan Wang, Miaoqing Huang, Tarek El-Ghazawi

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Tags: Computer science, CUDA, GPU cluster, nVidia, Performance, Programming techniques, Tesla C1060, Tesla C2070

November 19, 2011 by hgpu

ShoveRand: a model-driven framework to easily generate random numbers on GP-GPU

Jonathan Passerat-Palmbach, Claude Mazel, David R.C. Hill

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Tags: Computer science, CUDA, nVidia, Pseudo-random number generators, Stochastic simulation, Tesla C1060

November 19, 2011 by hgpu

SGPU 2: a runtime system for using large applications on clusters of hybrid nodes

Matthieu Ospici, Dimitri Komatitsch, Jean-Francois Mehaut, Thierry Deutsch

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Tags: Benchmarking, Computer science, CUDA, Hybrid computing, nVidia, Tesla T10

November 19, 2011 by hgpu

Predictive Modeling and Analysis of OP2 on Distributed Memory GPU Clusters

G.R. Mudalige, M.B. Giles, C. Bertolli, P.H.J Kelly

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Tags: Benchmarking, Computer science, CUDA, Fluid dynamics, GPU cluster, Heterogeneous systems, nVidia, nVidia GeForce GTX 560 Ti, Performance, Tesla C2070

November 19, 2011 by hgpu

Tapping the supercomputer under your desk: Solving dynamic equilibrium models with graphics processors

Eric M. Aldrich, Jesus Fernandez-Villaverde, A. Ronald Gallant, Juan F. Rubio-Ramirez

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Tags: Algorithms, CUDA, Finance, nVidia, nVidia GeForce GTX 280, Package

November 18, 2011 by hgpu

Optimizing the multipole-to-local operator in the fast multipole method for graphical processing units

Toru Takahashi1, Cris Cecka, William Fong, Eric Darve

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Tags: Algorithms, CUDA, Fast multipole method, nVidia, Package, Physics, Tesla C1060, Tesla C2050

November 18, 2011 by hgpu

Dax Toolkit: A Proposed Framework for Data Analysis and Visualization at Extreme Scale

Kenneth Moreland, Utkarsh Ayachit, Berk Geveci, Kwan-Liu Ma

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Tags: Algorithms, Computer science, CUDA, nVidia, Programming techniques, Tesla C2050, Visualization

November 17, 2011 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

Efficient Support for Matrix Computations on Heterogeneous Multi-core and Multi-GPU Architectures

Optimizing Symmetric Dense Matrix-Vector Multiplication on GPUs

Using the High Productivity Language Chapel to Target GPGPU Architectures

Anisotropic mesh coarsening and refinement on GPU architecture

Exploiting concurrent kernel execution on graphic processing units

Towards Efficient GPU Sharing on Multicore Processors

ShoveRand: a model-driven framework to easily generate random numbers on GP-GPU

SGPU 2: a runtime system for using large applications on clusters of hybrid nodes

Predictive Modeling and Analysis of OP2 on Distributed Memory GPU Clusters

Tapping the supercomputer under your desk: Solving dynamic equilibrium models with graphics processors

Optimizing the multipole-to-local operator in the fast multipole method for graphical processing units

Dax Toolkit: A Proposed Framework for Data Analysis and Visualization at Extreme Scale

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