high performance computing on graphics processing units: hgpu.org

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Hypercubic Storage Layout and Transforms in Arbitrary Dimensions using GPUs and CUDA

K.A. Hawick and D.P. Playne

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Tags: Computer science, CUDA, Memory model, nVidia, nVidia GeForce GTX 295, Storage system

February 14, 2011 by hgpu

Comparing Intra- and Inter-Processor Parallelism on Multi-Core Cell Processors for Scientific Simulations

K.A. Hawick, A. Leist and D.P. Playne and M.J. Johnson

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Tags: Cell processor, Computer science, Playstation, Pseudo-random number generators

February 14, 2011 by hgpu

Biomolecular electrostatics using a fast multipole BEM on up to 512 GPUs and a billion unknowns

Rio Yokota, Jaydeep P. Bardhan, Matthew G. Knepley, L. A. Barba, Tsuyoshi Hamada

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Tags: Chemical Physics, Computational Physics, Computer science, CUDA, Electrostatics, Fast multipole method, Integral equations, nVidia, nVidia GeForce GTX 295, Package

February 14, 2011 by hgpu

Speed and Portability issues for Random Number Generation on Graphical Processing Units with CUDA and other Processing Accelerators

K.A. Hawick, A. Leist, D.P. Playne, M.J. Johnson

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Tags: Cell processor, Computer science, Monte Carlo simulation, nVidia, nVidia GeForce GTX 295, Pseudo-random number generators

February 13, 2011 by hgpu

Automated and parallel code generation for finite-differencing stencils with arbitrary data types

K.A. Hawick and D.P. Playne

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Tags: Code generation, Computer science, CUDA, Finite difference, nVidia, nVidia GeForce GTX 295, Partial differential equations, PDEs

February 13, 2011 by hgpu

Data-Parallelism and GPUs for Lattice Gas Fluid Simulations

M.G.B. Johnson, D.P. Playne and K.A. Hawick

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Tags: Cellular automata, Computer science, CUDA, Fluid simulation, nVidia, nVidia GeForce GT 220, nVidia GeForce GTX 210, nVidia GeForce GTX 260, nVidia GeForce GTX 295

February 13, 2011 by hgpu

GPU-based Multi-Volume Rendering of Complex Data in Neuroscience and Neurosurgery

Johanna Beyer

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Tags: Computed tomography, Computer science, CT, CUDA, Medicine, Neuroscience, nVidia, nVidia GeForce GTX 280, Rendering, Visualization

February 13, 2011 by hgpu

Comparison of GPU Architectures for Asynchronous Communication with Finite-Differencing Applications

D.P. Playne, K.A. Hawick

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Tags: Computer science, CUDA, Finite difference, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 480, Partial differential equations, PDEs

February 13, 2011 by hgpu

Auto-Generation of Parallel Finite-Differencing Code for MPI, TBB and CUDA

D. P. Playne, K. A. Hawick

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Tags: Code generation, Computer science, CUDA, Finite difference, MPI, nVidia, nVidia GeForce GTX 480

February 13, 2011 by hgpu

Copperhead: Compiling an embedded data parallel language

Bryan Catanzaro, Michael Garland and Kurt Keutzer

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Tags: Code generation, Compilers, Computer science, CUDA, High-level Languages, nVidia, nVidia GeForce GTX 480, Package

February 13, 2011 by hgpu

Efficient Sparse Voxel Octrees – Analysis, Extensions, and Implementation

Samuli Laine, Tero Karras

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Tags: 3D Graphics and Realism, Algorithms, Computer science, CUDA, nVidia, nVidia Quadro FX 5800, Package, Rendering

February 12, 2011 by hgpu

Efficient sparse voxel octrees

Samuli Laine, Tero Karras

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Tags: 3D Graphics and Realism, Algorithms, Computer science, CUDA, nVidia, nVidia Quadro FX 5800, Package, Rendering

February 12, 2011 by hgpu

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

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high performance computing on graphics processing units: hgpu.org

Hypercubic Storage Layout and Transforms in Arbitrary Dimensions using GPUs and CUDA

Comparing Intra- and Inter-Processor Parallelism on Multi-Core Cell Processors for Scientific Simulations

Biomolecular electrostatics using a fast multipole BEM on up to 512 GPUs and a billion unknowns

Speed and Portability issues for Random Number Generation on Graphical Processing Units with CUDA and other Processing Accelerators

Automated and parallel code generation for finite-differencing stencils with arbitrary data types

Data-Parallelism and GPUs for Lattice Gas Fluid Simulations

GPU-based Multi-Volume Rendering of Complex Data in Neuroscience and Neurosurgery

Comparison of GPU Architectures for Asynchronous Communication with Finite-Differencing Applications

Auto-Generation of Parallel Finite-Differencing Code for MPI, TBB and CUDA

Copperhead: Compiling an embedded data parallel language

Efficient Sparse Voxel Octrees – Analysis, Extensions, and Implementation

Efficient sparse voxel octrees

Recent source codes

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

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