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hgpu.org » Programming » CUDA

GKLEE: Concolic Verification and Test Generation for GPUs

Guodong Li, Peng Li, Geof Sawaya, Ganesh Gopalakrishnan, Indradeep Ghosh, Sreeranga P. Rajan

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Programming techniques

January 12, 2012 by hgpu

Incomplete-LU and Cholesky Preconditioned Iterative Methods Using CUSPARSE and CUBLAS

Maxim Naumov

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Tags: Algorithms, Computer science, CUBLAS, CUDA, Factorization, Linear Algebra, nVidia, Tesla C2050

January 12, 2012 by hgpu

Optimization of power consumption in the iterative solution of sparse linear systems on graphics processors

Hartwig Anzt, Maribel Castillo, Juan C. Fernandez, Vincent Heuveline, Francisco D. Igual, Rafael Mayo and Enrique S. Quintana-Orti

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Tags: Algorithms, Computer science, CUDA, Energy-efficient computing, Mixed precision, nVidia, Tesla C1060

January 11, 2012 by hgpu

A parallel Genetic Programming algorithm for classification

Alberto Cano, Amelia Zafra, Sebastian Ventura

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Tags: Algorithms, Computer science, CUDA, Genetic programming, nVidia, nVidia GeForce GTX 480

January 11, 2012 by hgpu

Analysis and optimization of power consumption in the iterative solution of sparse linear systems on multi-core and many-core platforms

Hartwig Anzt, Vincent Heuveline, Jose I. Aliaga, Maribel Castillo, Juan C. Fernandez, Rafael Mayo, Enrique S. Quintana-Orti

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Tags: Algorithms, Computer science, CUDA, Energy-efficient computing, nVidia, Optimization, Sparse linear iterative solvers, Tesla C1060

January 11, 2012 by hgpu

Exponential integrators on graphic processing units

Lukas Einkemmer

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce 9800 GTX, Tesla C1060, Thesis

January 11, 2012 by hgpu

Efficient Ray Tracing of Dynamic Scenes on the GPU

Asger Dam Hoedt

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Tags: 3D Graphics and Realism, Computer science, CUDA, KD-tree, nVidia, nVidia GeForce GTX 330 M, Raytracing, Thesis

January 11, 2012 by hgpu

Implementing Genetic Algorithms to CUDA Environment Using Data Parallelization

Masashi Oiso, Yoshiyuki Matsumura, Toshiyuki Yasuda, Kazuhiro Ohkura

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Tags: Algorithms, Computer science, CUDA, Genetic programming, nVidia, nVidia GeForce GTX 280

January 11, 2012 by hgpu

Parallel LDPC decoding using CUDA and OpenMP

Joo-Yul Park, Ki-Seok Chung

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce 8800 GT, Parity-check codes

January 11, 2012 by hgpu

Massively Parallel GPU Computing of Continuum Robotic Dynamics

Roberto Orellana

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 8800 GT, Thesis

January 11, 2012 by hgpu

A Nearest Neighbor Data Structure for Graphics Hardware

Lawrence Cayton

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Tags: Algorithms, Computer science, CUDA, Data Structures and Algorithms, Databases, Nearest neighbour, nVidia, Tesla C2050

January 11, 2012 by hgpu

MetaBinG: Using GPUs to Accelerate Metagenomic Sequence Classification

Peng Jia, Liming Xuan, Lei Liu, Chaochun Wei

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Tags: Biology, Computational biology, CUDA, Genetics, nVidia, Package, Tesla C1060

January 11, 2012 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

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high performance computing on graphics processing units: hgpu.org

Programming

GKLEE: Concolic Verification and Test Generation for GPUs

Incomplete-LU and Cholesky Preconditioned Iterative Methods Using CUSPARSE and CUBLAS

Optimization of power consumption in the iterative solution of sparse linear systems on graphics processors

A parallel Genetic Programming algorithm for classification

Analysis and optimization of power consumption in the iterative solution of sparse linear systems on multi-core and many-core platforms

Exponential integrators on graphic processing units

Efficient Ray Tracing of Dynamic Scenes on the GPU

Implementing Genetic Algorithms to CUDA Environment Using Data Parallelization

Parallel LDPC decoding using CUDA and OpenMP

Massively Parallel GPU Computing of Continuum Robotic Dynamics

A Nearest Neighbor Data Structure for Graphics Hardware

MetaBinG: Using GPUs to Accelerate Metagenomic Sequence Classification

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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