high performance computing on graphics processing units: hgpu.org

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High performance discrete Fourier transforms on graphics processors

Naga K. Govindaraju, Brandon Lloyd, Yuri Dotsenko, Burton Smith, John Manferdelli

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Tags: Computer science, CUDA, FFT, nVidia, nVidia GeForce 8800 GTS, nVidia GeForce 8800 GTX, nVidia GeForce GTX 280

November 3, 2010 by hgpu

Improving Performance of Matrix Multiplication and FFT on GPU

Xiang Cui, Yifeng Chen, Hong Mei

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Tags: Computer science, CUDA, FFT, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX 280

November 2, 2010 by hgpu

A Memory Model for Scientific Algorithms on Graphics Processors

Naga K. Govindaraju, Scott Larsen, Jim Gray, Dinesh Manocha

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Tags: Algorithms, Computer science, FFT, Memory model, nVidia, nVidia GeForce 6800 Ultra, nVidia GeForce 7800 GTX, nVidia GeForce 7900 GTX, Sorting

October 30, 2010 by hgpu

An efficient, model-based CPU-GPU heterogeneous FFT library

Y. Ogata, T. Endo, N. Maruyama, S. Matsuoka

Tags: Computer science, FFT

October 28, 2010 by hgpu

cuInspiral: prototype gravitational waves detection pipeline fully coded on GPU using CUDA

Leone B. Bosi

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Tags: Computational Physics, CUDA, FFT, General Relativity and Quantum Cosmology, nVidia, nVidia GeForce GTX 275, Physics

October 28, 2010 by hgpu

GPU-Based FFT Computation for Multi-Gigabit WirelessHD Baseband Processing

Nicholas Hinitt, Taskin Kocak

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Tags: Computer science, CUDA, FFT, nVidia

October 27, 2010 by hgpu

The FFT on a GPU

Kenneth Moreland, Edward Angel

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Tags: Computer science, Computer vision, FFT, Image processing

October 27, 2010 by hgpu

CL4SE: A Context Learning Benchmark For Software Engineering Tasks

CodeScaler: Scaling Code LLM Training and Test-Time Inference via Execution-Free Reward Models

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

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high performance computing on graphics processing units: hgpu.org

High performance discrete Fourier transforms on graphics processors

Improving Performance of Matrix Multiplication and FFT on GPU

A Memory Model for Scientific Algorithms on Graphics Processors

An efficient, model-based CPU-GPU heterogeneous FFT library

cuInspiral: prototype gravitational waves detection pipeline fully coded on GPU using CUDA

GPU-Based FFT Computation for Multi-Gigabit WirelessHD Baseband Processing

The FFT on a GPU

Recent source codes

CL4SE: A Context Learning Benchmark For Software Engineering Tasks

CodeScaler: Scaling Code LLM Training and Test-Time Inference via Execution-Free Reward Models

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

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