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Memory Bandwidth Efficient Two-Dimensional Fast Fourier Transform Algorithm and Implementation for Large Problem Sizes

Berkin Akin, Peter A. Milder, Franz Franchetti, James C. Hoe

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Tags: Algorithms, Computer science, CUDA, Electronic design automation, FFT, FPGA, nVidia, nVidia GeForce GTX 480

April 23, 2012 by hgpu

Computing Performance Benchmarks among CPU, GPU, and FPGA

Christopher Robert Cullinan, Timothy Richard Frattesi, Christopher Michael Wyant

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Tags: Algorithms, Benchmarking, Computer science, CUDA, FPGA, nVidia, nVidia GeForce 9800 GTX, nVidia GeForce GTX 460, Performance

April 21, 2012 by hgpu

Fast Universal Background Model (UBM) Training on GPUs using Compute Unified Device Architecture (CUDA)

M. Azhari, C. Ergun

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Tags: Algorithms, Clustering, Computer science, CUDA, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 570

April 21, 2012 by hgpu

An On-Demand Fast Parallel Pseudo Random Number Generator with Applications

Dip Sankar Banerjee, Aman Kumar Bahl, Kishore Kothapalli

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Tags: Algorithms, Computer science, CUDA, List ranking, Monte Carlo simulation, nVidia, Pseudo-random number generators, Tesla C1060

April 21, 2012 by hgpu

Image Convolution Processing: a GPU versus FPGA Comparison

Lucas M. Russo, Emerson C. Pedrino, Edilson Kato, Valentin Obac Roda

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Tags: CUDA, FPGA, Image processing, nVidia, nVidia GeForce GTX 295, Performance

April 21, 2012 by hgpu

Fast Collision Culling in Large-Scale Environments Using GPU Mapping Function

Quentin Avril, Valerie Gouranton, Bruno Arnaldi

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Tags: 3D Graphics and Realism, Algorithms, Computer science, CUDA, nVidia, nVidia Quadro FX 3600 M

April 21, 2012 by hgpu

An Automatic OpenCL Compute Kernel Generator for Basic Linear Algebra Operations

Philippe Tillet, Karl Rupp, Siegfried Selberherr

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Tags: Algorithms, Code generation, Computer science, Linear Algebra, nVidia, nVidia GeForce GTX 470, OpenCL, Package

April 21, 2012 by hgpu

Priority-Based Task Management in a GPGPU Megakernel

Bernhard Kerbl

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Tags: Algorithms, Computer science, CUDA, nVidia, Performance, Raytracing, Rendering, Sorting

April 21, 2012 by hgpu

Multicore Processing for Classification and Clustering Algorithms

V. Vaitheeshwaran, Kapil Kumar Nagwanshi, T. V. Rao

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Tags: Algorithms, ATI, ATI Radeon HD 5870, Clustering, Computer science, CUDA, Data mining, nVidia, nVidia GeForce 8800 GTX, OpenCL

April 21, 2012 by hgpu

Genetic Algorithm Modeling with GPU Parallel Computing Technology

S. Cavuoti, M. Garofalo, M. Brescia, A. Pescape, G. Longo, G. Ventre

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Tags: Algorithms, Astrophysics, Computer science, CUDA, Data mining, Genetic programming, Machine learning, nVidia, nVidia GeForce GT 540 M

April 21, 2012 by hgpu

Graphical Processing Units (GPU)-based modeling for Acoustic and Ultrasonic NDE

Nahas Cheruvallykudy, Krishnan Balasubramaniam, Prabhu Rajagopal

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Tags: Acoustics, CUDA, FDTD, Finite difference, Finite-difference time-domain, nVidia, nVidia GeForce 9800 GT, Physics

April 21, 2012 by hgpu

Performance Exploration of Selected Manually and Automatically Parallelized Codes on GPUs

Franz Xaver Bayerl

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Tags: Code generation, Computer science, CUBLAS, CUDA, Matrix multiplication, nVidia, nVidia GeForce 9800 GTX, nVidia GeForce GTX 560 Ti, Performance, Thesis

April 19, 2012 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

Memory Bandwidth Efficient Two-Dimensional Fast Fourier Transform Algorithm and Implementation for Large Problem Sizes

Computing Performance Benchmarks among CPU, GPU, and FPGA

Fast Universal Background Model (UBM) Training on GPUs using Compute Unified Device Architecture (CUDA)

Image Convolution Processing: a GPU versus FPGA Comparison

Fast Collision Culling in Large-Scale Environments Using GPU Mapping Function

An Automatic OpenCL Compute Kernel Generator for Basic Linear Algebra Operations

Priority-Based Task Management in a GPGPU Megakernel

Multicore Processing for Classification and Clustering Algorithms

Genetic Algorithm Modeling with GPU Parallel Computing Technology

Graphical Processing Units (GPU)-based modeling for Acoustic and Ultrasonic NDE

Performance Exploration of Selected Manually and Automatically Parallelized Codes on GPUs

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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