high performance computing on graphics processing units: hgpu.org

hgpu.org » Computer science

The Virtual Marathon: Parallel Computing Supports Crowd Simulations

Erdal Yilmaz, Veysi Isler, Yasemin Y. Cetin

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Tags: Computer science, nVidia, nVidia GeForce GTX 280, OpenGL, Visualization

November 27, 2010 by hgpu

Accuracy and performance of graphics processors: A Quantum Monte Carlo application case study

Jeremy S. Meredith, Gonzalo Alvarez, Thomas A. Maier, Thomas C. Schulthess, Jeffrey S. Vetter

Tags: Computer science, Monte Carlo simulation, Performance

November 27, 2010 by hgpu

Triangular matrix inversion on Graphics Processing Unit

Florian Ries, Tommaso De Marco, Matteo Zivieri, Roberto Guerrieri

Tags: Computer science, CUDA, Linear Algebra, Matrix inversion, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 295

November 26, 2010 by hgpu

Fast Disk Encryption through GPGPU Acceleration

Giovanni Agosta, Alessandro Barenghi, Fabrizio D. Santis, Andrea D. Biagio, Gerardo Pelosi

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 260, Package, Security

November 26, 2010 by hgpu

On the energy efficiency of graphics processing units for scientific computing

S. Huang, S. Xiao, W. Feng

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Tags: Computer science, CUDA, Energy-efficient computing, nVidia, nVidia GeForce GTX 280

November 26, 2010 by hgpu

Compute Unified Device Architecture Application Suitability

Wen M. Hwu, Christopher Rodrigues, Shane Ryoo, John Stratton

Tags: Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX

November 25, 2010 by hgpu

A Superresolution Framework for High-Accuracy Multiview Reconstruction

Bastian Goldlucke, Daniel Cremers

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Tags: Computer science, Optimization, PDEs

November 25, 2010 by hgpu

Novel Computing Architectures

Volodymyr Kindratenko

Tags: Cell processor, Computer science, FPGA, Review

November 25, 2010 by hgpu

A particle-based method for viscoelastic fluids animation

Yuanzhang Chang, Kai Bao, Youquan Liu, Jian Zhu, Enhua Wu

Tags: 3D Graphics and Realism, Computer science, Fluid dynamics, Rendering

November 25, 2010 by hgpu

Designing and optimizing compute kernels on NVIDIA GPUs

Damir A. Jamsek

Tags: CAD, Computer science, CUDA, nVidia

November 25, 2010 by hgpu

GL4D: A GPU-based Architecture for Interactive 4D Visualization

A. Chu, Chi-Wing Fu, A. Hanson, Pheng-Ann Heng

Tags: Computer science, nVidia, OpenGL, Visualization

November 25, 2010 by hgpu

Fast minimum spanning tree for large graphs on the GPU

Vibhav Vineet, Pawan Harish, Suryakant Patidar, P. J. Narayanan

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Tags: Algorithms, Computer science, CUDA, Graph theory, nVidia, Tesla S1070

November 25, 2010 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

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high performance computing on graphics processing units: hgpu.org

The Virtual Marathon: Parallel Computing Supports Crowd Simulations

Accuracy and performance of graphics processors: A Quantum Monte Carlo application case study

Triangular matrix inversion on Graphics Processing Unit

Fast Disk Encryption through GPGPU Acceleration

On the energy efficiency of graphics processing units for scientific computing

Compute Unified Device Architecture Application Suitability

A Superresolution Framework for High-Accuracy Multiview Reconstruction

Novel Computing Architectures

A particle-based method for viscoelastic fluids animation

Designing and optimizing compute kernels on NVIDIA GPUs

GL4D: A GPU-based Architecture for Interactive 4D Visualization

Fast minimum spanning tree for large graphs on the GPU

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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