high performance computing on graphics processing units: hgpu.org

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G-NetMon: A GPU-accelerated Network Performance Monitoring System

Wenji Wu, Phil DeMar, Don Holmgren, Amitoj Singh

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Tags: Computer science, CUDA, GPU cluster, Networking and Internet Architecture, nVidia, Tesla C2070

August 9, 2011 by hgpu

G-NetMon: A GPU-accelerated Network Performance Monitoring System for Large Scale Scientific Collaborations

Wenji Wu, Phil DeMar, Don Holmgren, Amitoj Singh, Ruth Pordes

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Tags: Computer science, CUDA, GPU cluster, Networking and Internet Architecture, nVidia, Tesla C2070

August 9, 2011 by hgpu

Real-Time All-in-Focus Video-Based Rendering Using A Network Camera Array

Yuichi Taguchi, Keita Takahashi, Takeshi Naemura

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Tags: Algorithms, Cg, Computer science, Image generation, nVidia, nVidia GeForce 8800 Ultra, OpenGL, Real-time graphics, Rendering

August 9, 2011 by hgpu

Performance Evaluation of Feature Extraction Algorithm on GPGPU

Namdev Sawant, Dinesh Kulkarni

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Tags: Algorithms, Computer vision, CUDA, Image processing, nVidia, Tesla C1060

August 9, 2011 by hgpu

Optimization of parallel Genetic Algorithms for nVidia GPUs

Mohamed Wahib, Asim Munawar, Masaharu Munetomo, Kiyoshi Akama

Tags: Algorithms, Computer science, Evolutionary Computations, Genetic programming, nVidia, Optimization, Review, Tesla C1060

August 9, 2011 by hgpu

Performance Comparison with OpenMP Parallelization for Multi-core Systems

Chao-Tung Yang, Tzu-Chieh Chang, Hsien-Yi Wang, William C.C. Chu, Chih-Hung Chang

Tags: Code generation, Compilers, Computer science, CUDA, Data parallelism, Fortran, nVidia, OpenCL, OpenMP, Performance

August 8, 2011 by hgpu

AES finalists implementation for GPU and multi-core CPU based on OpenCL

Xingliang Wang, Xiaochao Li, Mei Zou, Jun Zhou

Tags: AES, Benchmarking, Computer science, CUDA, nVidia, OpenCL, Security

August 8, 2011 by hgpu

DC Power Flow Based Contingency Analysis Using Graphics Processing Units (thesis)

Anupam Gopal

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Tags: Cg, Computer science, nVidia, nVidia GeForce 7800 GTX, OpenGL, Power Systems Simulation, Thesis

August 8, 2011 by hgpu

DC Power Flow Based Contingency Analysis Using Graphics Processing Units

Anupam Gopal, Dagmar Niebur, Suresh Venkatasubramanian

Tags: Cg, Computer science, nVidia, nVidia GeForce 7800 GTX, OpenGL, Power Systems Simulation

August 8, 2011 by hgpu

GPU implementation of a Helmholtz Krylov Solver preconditioned by a shifted Laplace multigrid method

H. Knibbe, C. W. Oosterlee, C. Vuik

Tags: CUDA, Finite difference, Mathematics, nVidia

August 8, 2011 by hgpu

On the GPGPU parallelization issues of finite element approximate inverse preconditioning

C. K. Filelis-Papadopoulos, G. A. Gravvanis, P. I. Matskanidis, K. M. Giannoutakis

Tags: Algorithms, Computer science, CUDA, Finite element method, nVidia, Sparse linear iterative solvers

August 8, 2011 by hgpu

PUGACE, a cellular Evolutionary Algorithm framework on GPUs

Nicolas Soca, Jose Luis Blengio, Martin Pedemonte, Pablo Ezzatti

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Tags: Algorithms, Computer science, CUDA, Evolutionary Computations, Metaheuristics, nVidia, nVidia GeForce 9800 GTX, Optimization

August 7, 2011 by hgpu

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

Agentic Code Optimization via Compiler-LLM Cooperation

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

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

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

LLMQ: Efficient Lower-Precision LLM Training for Consumer GPUs

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

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high performance computing on graphics processing units: hgpu.org

G-NetMon: A GPU-accelerated Network Performance Monitoring System

G-NetMon: A GPU-accelerated Network Performance Monitoring System for Large Scale Scientific Collaborations

Real-Time All-in-Focus Video-Based Rendering Using A Network Camera Array

Performance Evaluation of Feature Extraction Algorithm on GPGPU

Optimization of parallel Genetic Algorithms for nVidia GPUs

Performance Comparison with OpenMP Parallelization for Multi-core Systems

AES finalists implementation for GPU and multi-core CPU based on OpenCL

DC Power Flow Based Contingency Analysis Using Graphics Processing Units (thesis)

DC Power Flow Based Contingency Analysis Using Graphics Processing Units

GPU implementation of a Helmholtz Krylov Solver preconditioned by a shifted Laplace multigrid method

On the GPGPU parallelization issues of finite element approximate inverse preconditioning

PUGACE, a cellular Evolutionary Algorithm framework on GPUs

Recent source codes

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

Device Virtual Machine (DVM)

Agentic Code Optimization via Compiler-LLM Cooperation

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

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

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

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

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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