high performance computing on graphics processing units: hgpu.org

Applications

hgpu.org » Applications

DSPSR: Digital Signal Processing Software for Pulsar Astronomy

W. van Straten, M. Bailes

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Tags: Astrophysics, CUDA, FFT, Instrumentation and Methods for Astrophysics, nVidia, Package, Physics, Tesla C2050

December 21, 2010 by hgpu

Efficient, High-Quality Bayer Demosaic Filtering on GPUs

Morgan McGuire

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Tags: GLSL, Image processing, nVidia, nVidia GeForce 8600 M GT, OpenGL, Package

December 18, 2010 by hgpu

Monte Carlo simulation of photon migration in 3D turbid media accelerated by graphics processing units

Qianqian Fang, David A. Boas

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Tags: CUDA, Monte Carlo simulation, nVidia, nVidia GeForce 8800 GT, Optics, Package, Physics

December 17, 2010 by hgpu

GPU Parallelization of Algebraic Dynamic Programming

Peter Steffen, Robert Giegerich, Mathieu Giraud

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 280, Package, Presentation, Programming techniques

December 17, 2010 by hgpu

GPU-based Monte Carlo simulation for light propagation in complex heterogeneous tissues

Nunu Ren, Jimin Liang, Xiaochao Qu, Jianfeng Li, Bingjia Lu, Jie Tian

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Tags: CUDA, Monte Carlo simulation, nVidia, nVidia GeForce GTX 260, Optics, Package, Physics

December 17, 2010 by hgpu

OpenMM: A Hardware-Independent Framework for Molecular Simulations

Peter Eastman, Vijay Pande

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Tags: Chemistry, Computational chemistry, Molecular modeling, Package

December 16, 2010 by hgpu

Genetic programming on graphics processing units

Denis Robilliard, Virginie Marion-Poty, Cyril Fonlupt

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, Package

December 15, 2010 by hgpu

gProximity: Hierarchical GPU-based Operations for Collision and Distance Queries

C. Lauterbach, Q. Mo, D. Manocha

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Tags: Algorithms, Collision detection, Computer science, CUDA, nVidia, nVidia GeForce GTX 285, Package

December 14, 2010 by hgpu

A generic library for structured real-time computations: GPU implementation applied to retinal and cortical vision processes

Jean-Charles Quinton

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Tags: ATI, ATI Mobility Radeon 9700, Computer science, Computer vision, Image processing, OpenGL, Package

December 13, 2010 by hgpu

Accelerating Compute-Intensive Applications with GPUs and FPGAs

Shuai Che, Jie Li, Jeremy W. Sheaffer, Kevin Skadron, John Lach

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Tags: Architecture, Computer science, CUDA, FPGA, nVidia, nVidia GeForce 8800 GTX, Package, Performance

December 13, 2010 by hgpu

Real-world comparison of CPU and GPU implementations of SNPrank: a network analysis tool for GWAS

Nicholas A. Davis, Ahwan Pandey, B. A. McKinney

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Tags: Bioinformatics, Biology, CUDA, Genetics, Medicine, nVidia, Package, Tesla M1060

December 13, 2010 by hgpu

SWPS3 – fast multi-threaded vectorized Smith-Waterman for IBM Cell/B.E. and x86/SSE2

Adam Szalkowski, Christian Ledergerber, Philipp Kraehenbuehl, Christophe Dessimoz

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Tags: Biology, Cell processor, CUDA, nVidia, nVidia GeForce 8800 GTX, Optimization, Package, Sequence alignment, Smith-Waterman algorithm

December 12, 2010 by hgpu

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

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

LLM.Q: Quantized LLM training in pure CUDA/C++

LLMQ: Efficient Lower-Precision LLM Training for Consumer GPUs

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

True 4-Bit Quantized CNN Training on CPU

True 4-Bit Quantized Convolutional Neural Network Training on CPU: Achieving Full-Precision Parity

cuFuzz: A GPU-oriented coverage-guided fuzzer for userland CUDA application

Hunting CUDA Bugs at Scale with cuFuzz

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

MSKernelBench & CUDAMaster

Making LLMs Optimize Multi-Scenario CUDA Kernels Like Experts

EvoScientist: Harness Vibe Research with Self-evolving AI Scientists

EvoScientist: Towards Multi-Agent Evolving AI Scientists for End-to-End Scientific Discovery

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build & Test Pipeline of Code Repositories on ANY Language and ANY Platform

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high performance computing on graphics processing units: hgpu.org

Applications

DSPSR: Digital Signal Processing Software for Pulsar Astronomy

Efficient, High-Quality Bayer Demosaic Filtering on GPUs

Monte Carlo simulation of photon migration in 3D turbid media accelerated by graphics processing units

GPU Parallelization of Algebraic Dynamic Programming

GPU-based Monte Carlo simulation for light propagation in complex heterogeneous tissues

OpenMM: A Hardware-Independent Framework for Molecular Simulations

Genetic programming on graphics processing units

gProximity: Hierarchical GPU-based Operations for Collision and Distance Queries

A generic library for structured real-time computations: GPU implementation applied to retinal and cortical vision processes

Accelerating Compute-Intensive Applications with GPUs and FPGAs

Real-world comparison of CPU and GPU implementations of SNPrank: a network analysis tool for GWAS

SWPS3 – fast multi-threaded vectorized Smith-Waterman for IBM Cell/B.E. and x86/SSE2

Recent source codes

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

LLM.Q: Quantized LLM training in pure CUDA/C++

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

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

MSKernelBench & CUDAMaster

EvoScientist: Harness Vibe Research with Self-evolving AI Scientists

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

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