high performance computing on graphics processing units: hgpu.org

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Scalable Clustering Using Graphics Processors

Feng Cao, Anthony Tung, Aoying Zhou

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Tags: Algorithms, Cg, Clustering, Computer science, nVidia, nVidia GeForce 6800 GT, OpenGL

April 22, 2011 by hgpu

GPU accelerated simulations of 3D deterministic particle transport using discrete ordinates method

Chunye Gong, Jie Liu, Lihua Chi, Haowei Huang, Jingyue Fang, Zhenghu Gong

Tags: Boltzmann equation, Computational Physics, CUDA, nVidia, Physics, Tesla M2050

April 22, 2011 by hgpu

Pretty Good Accuracy in Matrix Multiplication with GPUs

Matthew Badin, Lubomir Bic, Michael Dillencourt, Alexandru Nicolau

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Tags: Computer science, CUDA, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX 285

April 21, 2011 by hgpu

Fast Variable Center-Biased Windowing for High-Speed Stereo on Programmable Graphics Hardware

Jiangbo Lu, G. Lafruit, F. Catthoor

Tags: Image processing, nVidia, nVidia GeForce 7900, Optimization

April 21, 2011 by hgpu

Using graphics processors to accelerate the computation of the matrix inverse

P. Ezzatti, E. Quintana-Orti, A. Remon

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Tags: Computer science, Linear Algebra, Matrix inversion

April 21, 2011 by hgpu

Automatically generating and tuning GPU code for sparse matrix-vector multiplication from a high-level representation

Dominik Grewe, Anton Lokhmotov

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Tags: ATI, ATI Radeon HD 5970, Code generation, Computer science, CUDA, High-level Languages, Linear Algebra, nVidia, OpenCL, Sparse matrix, Tesla S1070

April 21, 2011 by hgpu

Automatically Tuning Sparse Matrix-Vector Multiplication for GPU Architectures

Alexander Monakov, Anton Lokhmotov, Arutyun Avetisyan

Tags: Computer science, Partial differential equations, PDEs, Sparse matrix

April 21, 2011 by hgpu

Assessment of GPU computational enhancement to a 2D flood model

Alfred J. Kalyanapu, Siddharth Shankar, Eric R. Pardyjak, David R. Judi, Steven J. Burian

Tags: CUDA, Earth and Space Sciences, Fluid simulation, nVidia

April 21, 2011 by hgpu

Solving knapsack problems on GPU

V. Boyer, D. El Baz, M. Elkihel

Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 260, Optimization

April 21, 2011 by hgpu

Auto-Tuning CUDA Parameters for Sparse Matrix-Vector Multiplication on GPUs

Ping Guo, Liqiang Wang

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Tags: Computer science, CUDA, Linear Algebra, nVidia, nVidia GeForce 9500 GTX, nVidia GeForce GTX 295, Sparse matrix

April 21, 2011 by hgpu

A performance prediction model for the CUDA GPGPU platform

Kishore Kothapalli, Rishabh Mukherjee, M. Suhail Rehman, Suryakant Patidar, P. J. Narayanan, Kannan Srinathan

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Tags: Computer science, CUDA, Matrix multiplication, nVidia, nVidia GeForce GTX 280, Performance

April 21, 2011 by hgpu

Fine-sorting One-dimensional Particle-In-Cell Algorithm with Monte-Carlo Collisions on a Graphics Processing Unit

Philipp Mertmann, Denis Eremin, Thomas Mussenbrock, Ralf Peter Brinkmann, Peter Awakowicz

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Tags: Algorithms, Computational Physics, CUDA, nVidia, nVidia GeForce GTX 480, Particle-in-cell methods, Physics, Plasma physics, Sorting

April 21, 2011 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

Applications

Scalable Clustering Using Graphics Processors

GPU accelerated simulations of 3D deterministic particle transport using discrete ordinates method

Pretty Good Accuracy in Matrix Multiplication with GPUs

Fast Variable Center-Biased Windowing for High-Speed Stereo on Programmable Graphics Hardware

Using graphics processors to accelerate the computation of the matrix inverse

Automatically generating and tuning GPU code for sparse matrix-vector multiplication from a high-level representation

Automatically Tuning Sparse Matrix-Vector Multiplication for GPU Architectures

Assessment of GPU computational enhancement to a 2D flood model

Solving knapsack problems on GPU

Auto-Tuning CUDA Parameters for Sparse Matrix-Vector Multiplication on GPUs

A performance prediction model for the CUDA GPGPU platform

Fine-sorting One-dimensional Particle-In-Cell Algorithm with Monte-Carlo Collisions on a Graphics Processing Unit

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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