high performance computing on graphics processing units: hgpu.org

Applications

hgpu.org » Applications

GPU-Accelerated SPH Model for Water Waves and Other Free Surface Flows

Robert Anthony Dalrymple, Alexis Herault, Giuseppe Bilotta, Rozita Jalali Farahani

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Tags: CUDA, Fluid dynamics, nVidia, OpenGL, Package, Visualization

November 30, 2011 by hgpu

GPGPU Volume Classification using SimpleOpenCL

Oscar Amoros, Sergio Escalera, Anna Puig, Maria Salamo

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Tags: Computer science, nVidia, nVidia GeForce GTX 470, OpenCL, Package, Visualization

November 29, 2011 by hgpu

Efficient Parallel Nonnegative Least Squares on Multicore Architectures

Yuancheng Luo, Ramani Duraiswami

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Tags: Algorithms, Computer science, CUDA, Factorization, Nonnegative matrix factorization, nVidia, Package, Tesla C2050

November 28, 2011 by hgpu

Towards paradisEO-MO-GPU: a framework for GPU-based local search metaheuristics

N. Melab, T-V. Luong, K. Boufaras, E-G. Talbi

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Tags: Computer science, CUDA, Metaheuristics, nVidia, nVidia GeForce GTX 480, Package

November 27, 2011 by hgpu

An Explicit Algorithm for Porous Media Flow Simulation using GPUs

D.N. Morozov, B.N. Chetverushkin, N.G. Churbanova, M.A. Trapeznikova

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Tags: Algorithms, CUDA, Finite difference, Fluid dynamics, GPU cluster, nVidia, nVidia GeForce GTX 295, Package

November 24, 2011 by hgpu

Accelerating QDP++/Chroma on GPUs

Frank Winter

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Tags: CUDA, High Energy Physics - Lattice, nVidia, nVidia GeForce GTX 480, Package, Physics, QCD, Tesla C2070

November 24, 2011 by hgpu

Dynamic adaptation and distribution of binaries to heterogeneous architectures

Espen Angell Kristiansen

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Tags: Computer science, CUDA, Distributed computing, Heterogeneous systems, nVidia, nVidia GeForce GT 220, nVidia GeForce GTX 280, OpenCL, Package, Playstation, Thesis

November 22, 2011 by hgpu

Application of GPGPU for Acceleration of Short DNA Sequence Alignment in Unipro UGENE Project

Konstantin Okonechinkov, German Grekhov, Konstantin Stepanyuk, Mikhail Fursov

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Tags: Algorithms, Bioinformatics, Biology, nVidia, OpenCL, Package, Sequence alignment, Tesla C1060, Tesla C2070

November 22, 2011 by hgpu

Conflux: Embedding Massively Parallel Semantics in a High-Level Programming Language

Eugene Burmako, Rauf Sadykhov

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Tags: Algorithms, Computer science, CUDA, High-level Languages, nVidia, Package

November 21, 2011 by hgpu

PATUS: A Code Generation and Autotuning Framework For Parallel Iterative Stencil Computations on Modern Microarchitectures

Matthias Christen, Olaf Schenk, Helmar Burkhart

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Tags: Code generation, Computer science, Java, nVidia, Optimization, Package, Tesla C2050

November 21, 2011 by hgpu

Implementing a Finite Difference-Based Real-time Sound Synthesizer using GPUs

Marc Sosnick, William Hsu

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Tags: Acoustics, Algorithms, Computer science, CUDA, Finite difference, nVidia, nVidia GeForce GT 330 M, Package, Physics

November 20, 2011 by hgpu

Autotuning GEMMs for Fermi

Jakub Kurzak, Stanimire Tomov, Jack Dongarra

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Tags: Benchmarking, BLAS, Computer science, CUDA, Linear Algebra, Mathematical Software, Matrix multiplication, nVidia, Package, Tesla S2050

November 20, 2011 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

GPU-Accelerated SPH Model for Water Waves and Other Free Surface Flows

GPGPU Volume Classification using SimpleOpenCL

Efficient Parallel Nonnegative Least Squares on Multicore Architectures

Towards paradisEO-MO-GPU: a framework for GPU-based local search metaheuristics

An Explicit Algorithm for Porous Media Flow Simulation using GPUs

Accelerating QDP++/Chroma on GPUs

Dynamic adaptation and distribution of binaries to heterogeneous architectures

Application of GPGPU for Acceleration of Short DNA Sequence Alignment in Unipro UGENE Project

Conflux: Embedding Massively Parallel Semantics in a High-Level Programming Language

PATUS: A Code Generation and Autotuning Framework For Parallel Iterative Stencil Computations on Modern Microarchitectures

Implementing a Finite Difference-Based Real-time Sound Synthesizer using GPUs

Autotuning GEMMs for Fermi

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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