high performance computing on graphics processing units: hgpu.org

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hgpu.org » Applications » Fluid dynamics

Lattice-Boltzmann Simulation of the Shallow-Water Equations with Fluid-Structure Interaction on Multi- and Manycore Processors

Markus Geveler, Dirk Ribbrock, Dominik Goddeke, and Stefan Turek

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Tags: Cell processor, CUDA, Fluid dynamics, GPU cluster, Lattice Boltzmann model, MPI, Navier-Stokes equations, NSEs, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 285, Package

March 1, 2011 by hgpu

A Simulation Suite for Lattice-Boltzmann based Real-Time CFD Applications Exploiting Multi-Level Parallelism on modern Multi- and Many-Core Architectures

Markus Geveler, Dirk Ribbrock, Sven Mallach, Dominik Goddeke

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Tags: Cell processor, CUDA, Fluid dynamics, GPU cluster, Lattice Boltzmann model, MPI, Navier-Stokes equations, NSEs, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 285, Package

March 1, 2011 by hgpu

SPH on GPU with CUDA

Alexis Herault, Giuseppe Bilotta, Robert A. Dalrymple

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Tags: CUDA, Fluid dynamics, Fluid simulation, nVidia, nVidia GeForce 8800 GT, nVidia GeForce GTX 280, Package, Particle simulation, Tesla C1060

February 1, 2011 by hgpu

Practical CFD Simulations on Programmable Graphics Hardware using SMAC

Carlos Eduardo Scheidegger, Joao L.D. Comba, Rudnei D. Da Cunha

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Tags: Cg, Fluid dynamics, Navier-Stokes equations, NSEs, nVidia, nVidia GeForce 6800 Ultra, nVidia GeForce FX 5900, OpenGL, Package

January 14, 2011 by hgpu

Fast fluid dynamics simulation on the GPU

Mark Harris

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Tags: Cg, Computer science, Fluid dynamics, Navier-Stokes equations, NSEs, nVidia, Package, Programming techniques

October 27, 2010 by hgpu

Kernel Library for LLM Serving

Compiler and Runtime Systems for Generative AI Models

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

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

Genten: Software for Generalized Tensor Decompositions by Sandia National Laboratories

A Performance Portable Matrix Free Dense MTTKRP in GenTen

Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR

Pinocchio: PINpointing Orbit Crossing Collapsed Hierarchical Objects

Accelerating cosmological simulations on GPUs: a portable approach using OpenMP

KernelCoder: trained on a curated dataset of reasoning traces and CUDA kernel pairs

ConCuR: Conciseness Makes State-of-the-Art Kernel Generation

VibeCodeHPC - Multi Agentic Vibe Coding for HPC

VibeCodeHPC: An Agent-Based Iterative Prompting Auto-Tuner for HPC Code Generation Using LLMs

Compile-Time Resource Safety for GPU APIs: A Low-Overhead Typestate Framework

exa-AMD: Exascale Accelerated Materials Discovery

exa-AMD: An Exascale-Ready Framework for Accelerating the Discovery and Design of Functional Materials

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high performance computing on graphics processing units: hgpu.org

Applications

Lattice-Boltzmann Simulation of the Shallow-Water Equations with Fluid-Structure Interaction on Multi- and Manycore Processors

A Simulation Suite for Lattice-Boltzmann based Real-Time CFD Applications Exploiting Multi-Level Parallelism on modern Multi- and Many-Core Architectures

SPH on GPU with CUDA

Practical CFD Simulations on Programmable Graphics Hardware using SMAC

Fast fluid dynamics simulation on the GPU

Recent source codes

Kernel Library for LLM Serving

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

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

Genten: Software for Generalized Tensor Decompositions by Sandia National Laboratories

Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR

Pinocchio: PINpointing Orbit Crossing Collapsed Hierarchical Objects

KernelCoder: trained on a curated dataset of reasoning traces and CUDA kernel pairs

VibeCodeHPC - Multi Agentic Vibe Coding for HPC

Compile-Time Resource Safety for GPU APIs: A Low-Overhead Typestate Framework

exa-AMD: Exascale Accelerated Materials Discovery

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