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Parallel implematation of flow and matching algorithms

Agnieszka Lupinska

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Tags: Algorithms, Computer science, CUDA, Graph theory, nVidia, nVidia GeForce GTX 560 Ti

October 31, 2011 by hgpu

Automatic CUDA Code Synthesis Framework for Multicore CPU and GPU architectures

Hanwoong Jung, Youngmin Yi, Soonhoi Ha

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Tags: Code generation, Computer science, CUDA, Data parallelism, Heterogeneous systems, nVidia, Tesla M2050

October 30, 2011 by hgpu

Accelerating Real-time processing of the ATST Adaptive Optics System using Coarse-grained Parallel Hardware Architectures

V. Venugopal, K. Richards, S. Barden, T. Rimmele, S. Gregory, L. Johnson

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Tags: Algorithms, Benchmarking, CUDA, DSP, FPGA, nVidia, Optics, Physics, Tesla C1060, Tesla C2050

October 30, 2011 by hgpu

A scalable hybrid algorithm based on domain decomposition and algebraic multigrid for solving partial differential equations on a cluster of CPU/GPUs

Li Luo, Chao Yang, Yubo Zhao, Xiao-Chuan Cai

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Tags: Algorithms, Computer science, CUDA, Differential equations, Factorization, FFT, Finite difference, GPU cluster, nVidia, Partial differential equations, PDEs, Tesla C1060

October 30, 2011 by hgpu

Optimizing and Auto-tuning Belief Propagation on the GPU

Scott Grauer-Gray, John Cavazos

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Tags: Compilers, Computer science, CUDA, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 470, Optimization, Tesla C870

October 30, 2011 by hgpu

Effective Parallelization of Non-bonded Interactions Kernel for Virtual Screening on GPUs

Gines D. Guerrero, Horacio Perez-Sanchez, Wolfgang Wenzel, Jose M. Cecilia, Jose M. Garcia

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Tags: Bioinformatics, Biology, CUDA, nVidia, Optimization, Pharmaceuticals, Tesla C1060

October 30, 2011 by hgpu

Improving Energy Efficiency of GPU based General-Purpose Scientific Computing through Automated Selection of Near Optimal Configurations

Xiaohan Ma, Marion Rincon, Zhigang Deng

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Tags: Algorithms, Benchmarking, Computer science, CUDA, Energy-efficient computing, nVidia, nVidia GeForce 8800 GT

October 30, 2011 by hgpu

Using GPUs to Accelerate Installed Antenna Performance Simulations

Tod Courtney, John E. Stone, Bob Kipp

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Tags: CUDA, Electrodynamics, nVidia, nVidia GeForce GTX 480, Raytracing

October 29, 2011 by hgpu

An Adaptive Framework for Managing Heterogeneous Many-Core Clusters

Muhammad Mustafa Rafique

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, MapReduce, nVidia, Prefetch, Software Engineering, Tesla C2050, Thesis

October 28, 2011 by hgpu

Compiling Stream Applications for Heterogeneous Architectures

Amir H. Hormati

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Tags: Compilers, Computer science, CUDA, FPGA, Heterogeneous systems, nVidia, nVidia GeForce GTX 285, Optimization, Tesla C2050, Thesis

October 28, 2011 by hgpu

Architecture-based Performance Evaluation of Genetic Algorithms on Multi/Many-core Systems

Long Zheng, Yanchao Lu, Mengwei Ding, Yao Shen, Minyi Guo, Song Guo

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

October 28, 2011 by hgpu

Matrix inversion speed up with CUDA

Jorge Soriano Pinedo

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Tags: Algorithms, Computer science, CUDA, Image reconstruction, Matrix inversion, Matrix multiplication, nVidia, Tesla C1060, Thesis

October 28, 2011 by hgpu

A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5

DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels

KernelGYM & Dr. Kernel: A distributed GPU environment and a collection of RL training methods to support RL for Kernel Generations

Dr. Kernel: Reinforcement Learning Done Right for Triton Kernel Generations

Theorizer: from the paper Generating Literature-Driven Scientific Discoveries at Scale

Generating Literature-Driven Scientific Theories at Scale

Nsight Python: a Python kernel profiling interface based on NVIDIA Nsight Tools

Nsight Python: A Python-First Profiling Toolkit for Seamless GPU Kernel Analysis (Tool)

Awesome LLM-Driven Kernel Generation

Towards Automated Kernel Generation in the Era of LLMs

See all packages

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high performance computing on graphics processing units: hgpu.org

Parallel implematation of flow and matching algorithms

Automatic CUDA Code Synthesis Framework for Multicore CPU and GPU architectures

Accelerating Real-time processing of the ATST Adaptive Optics System using Coarse-grained Parallel Hardware Architectures

A scalable hybrid algorithm based on domain decomposition and algebraic multigrid for solving partial differential equations on a cluster of CPU/GPUs

Optimizing and Auto-tuning Belief Propagation on the GPU

Effective Parallelization of Non-bonded Interactions Kernel for Virtual Screening on GPUs

Improving Energy Efficiency of GPU based General-Purpose Scientific Computing through Automated Selection of Near Optimal Configurations

Using GPUs to Accelerate Installed Antenna Performance Simulations

An Adaptive Framework for Managing Heterogeneous Many-Core Clusters

Compiling Stream Applications for Heterogeneous Architectures

Architecture-based Performance Evaluation of Genetic Algorithms on Multi/Many-core Systems

Matrix inversion speed up with CUDA

Recent source codes

A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5

DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels

KernelGYM & Dr. Kernel: A distributed GPU environment and a collection of RL training methods to support RL for Kernel Generations

Vortex-Optimized Light-weight Toolchain (VOLT)

SciDef: Automated Definition Extraction from Scientific Literature

bioagent-bench: Benchmark for evaluating LLM agents in bioinformatics

Benchmark suite for LLM inference on NVIDIA consumer GPUs

Theorizer: from the paper Generating Literature-Driven Scientific Discoveries at Scale

Nsight Python: a Python kernel profiling interface based on NVIDIA Nsight Tools

Awesome LLM-Driven Kernel Generation

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