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Markov Chain Monte Carlo on the GPU

Michael D. Dumont

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Tags: Algorithms, Computer science, Monte Carlo simulation, nVidia, nVidia GeForce GT 330 M, OpenCL, Thesis

January 21, 2012 by hgpu

A Practical Visualization Strategy for Large-Scale Supernovae CFD Simulations

Derek K. Gerstmann, Toby Potter, Michael Houston, Paul Bourke, Kwan-Liu Ma, Andreas Wicenec

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Tags: ATI, ATI Radeon HD 6750 M, Fluid dynamics, OpenCL, OpenGL, Visualization

January 21, 2012 by hgpu

Parallel FEM Simulation Using GPUs

Vishnukanthan Kandasamy

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Tags: Computer science, CUDA, FEM, Finite element method, MPI, nVidia

January 21, 2012 by hgpu

EASEA: A Generic Optimization Tool for GPU Machines in Asynchronous Island Model

Laurent A. Baumes, Frederic Kruger, Pierre Collet

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Tags: Algorithms, Computer science, CUDA, Evolutionary Computations, Materials Science, nVidia, nVidia GeForce GTX 275, Package

January 21, 2012 by hgpu

Plenoptic Rendering With Interactive Performance Using GPUs

Andrew Lumsdaine, Georgi Chunev, Todor Georgiev

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Tags: Algorithms, Image processing, nVidia, nVidia Quadro FX 5600, OpenGL, Rendering

January 21, 2012 by hgpu

Direct Visualization of Particle-Partition of Unity Data

M. Uffinger, M. A. Schweitzer, F. Sadlo, T. Ertl

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Tags: Algorithms, Computer science, GLSL, Image generation, nVidia, nVidia GeForce GTX 470, OpenGL, Visualization

January 21, 2012 by hgpu

The State of the Art in Interactive Global Illumination

Tobias Ritschel, Carsten Dachsbacher, Thorsten Grosch, Jan Kautz

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Tags: 3D Graphics and Realism, Algorithms, Computer science, Raytracing, Rendering, Review

January 21, 2012 by hgpu

Fast Graph Cuts using Shrink-Expand Reparameterization

Parikshit Sakurikar, P. J. Narayanan

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Tags: Algorithms, Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 580, Optimization

January 21, 2012 by hgpu

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

Automated Techniques for Enabling Efficient MPI Application Migration

Karolina Sarnowska-Upton, Andrew Grimshaw

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Tags: Computer science, MPI, Programming techniques

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

Agentic Code Optimization via Compiler-LLM Cooperation

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

DVM: Real-Time Kernel Generation for Dynamic AI Models

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

AutoKernel: Autonomous GPU Kernel Optimization via Iterative Agent-Driven Search

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

* * *

high performance computing on graphics processing units: hgpu.org

Applications

Markov Chain Monte Carlo on the GPU

A Practical Visualization Strategy for Large-Scale Supernovae CFD Simulations

Parallel FEM Simulation Using GPUs

EASEA: A Generic Optimization Tool for GPU Machines in Asynchronous Island Model

Plenoptic Rendering With Interactive Performance Using GPUs

Direct Visualization of Particle-Partition of Unity Data

The State of the Art in Interactive Global Illumination

Fast Graph Cuts using Shrink-Expand Reparameterization

On the Correctness of the SIMT Execution Model of GPUs

Automated Techniques for Enabling Efficient MPI Application Migration

Stochastic Progressive Photon Mapping for Dynamic Scenes

Recent source codes

Agentic Code Optimization via Compiler-LLM Cooperation

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

LLM.Q: Quantized LLM training in pure CUDA/C++

True 4-Bit Quantized CNN Training on CPU

cuFuzz: A GPU-oriented coverage-guided fuzzer for userland CUDA application

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

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