high performance computing on graphics processing units: hgpu.org

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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

In-process optical characterization method for sub-100-nm nanostructures

S. Kiess, M.Z. Shaikh, M. Gregoire, T. Bringewat, S. Simon, A. Tausendfreund, M. Zimmermann, G. Goch

Tags: Algorithms, Mesoscale and Nanoscale Physics, Optics, Physics

August 9, 2011 by hgpu

High-Performance Diagnostic Fault Simulation on GPUs

Min Li, Michael S. Hsiao

Tags: Computer science, Electronic design automation, Fault simulation

August 8, 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

The distributed diagonal force decomposition method for parallelizing molecular dynamics simulations

Urban Borstnik, Benjamin T. Miller, Bernard R. Brooks, Dusanka Janezic

Tags: Algorithms, Chemistry, Computational chemistry, Molecular dynamics, Performance

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

Structural, dynamic, and electrostatic properties of fully hydrated DMPC bilayers from molecular dynamics simulations accelerated with graphical processing units (GPUs)

Narayan Ganesan, Brad A. Bauer, Timothy R. Lucas, Sandeep Patel, Michela Taufer

Tags: Algorithms, Chemistry, Computational chemistry, Electrostatic potential, Electrostatics, Molecular dynamics

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

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

Optimization of parallel Genetic Algorithms for nVidia GPUs

In-process optical characterization method for sub-100-nm nanostructures

High-Performance Diagnostic Fault Simulation on GPUs

Performance Comparison with OpenMP Parallelization for Multi-core Systems

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

The distributed diagonal force decomposition method for parallelizing molecular dynamics simulations

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

Structural, dynamic, and electrostatic properties of fully hydrated DMPC bilayers from molecular dynamics simulations accelerated with graphical processing units (GPUs)

On the GPGPU parallelization issues of finite element approximate inverse preconditioning

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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