high performance computing on graphics processing units: hgpu.org

Applications

hgpu.org » Applications

Scheduling data flow program in xkaapi: A new affinity based Algorithm for Heterogeneous Architectures

Raphael Bleuse, Thierry Gautier, Joao V. F. Lima, Gregory Mounie, Denis Trystram

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

February 28, 2014 by hgpu

Extending the Generalized Fermat Prime Number Search Beyond One Million Digits Using GPUs

Iain Bethune, Michael Goetz

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Tags: CUDA, Mathematics, nVidia, nVidia GeForce GTX 460, Package

February 27, 2014 by hgpu

MICA: A fast short-read aligner that takes full advantage of Intel Many Integrated Core Architecture (MIC)

Sze-Hang Chan, Jeanno Cheung, Edward Wu, Heng Wang, Chi-Man Liu, Xiaoqian Zhu, Shaoliang Peng, Ruibang Luo, Tak-Wah Lam

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Tags: Bioinformatics, Biology, Intel Xeon Phi, Package

February 21, 2014 by hgpu

Fast Hamiltonian Monte Carlo Using GPU Computing

Andrew L. Beam, Sujit K. Ghosh, Jon Doyle

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Tags: Algorithms, Bayesian, CUDA, CULA, Monte Carlo simulation, nVidia, Package, Python, Statistics, Tesla K20

February 19, 2014 by hgpu

Optimizing Performance of Stencil Code with SPL Conqueror

Alexander Grebhahn, Norbert Siegmund, Sven Apel, Sebastian Kuckuk, Christian Schmitt, Harald Kostler

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Tags: Computer science, CUDA, Differential equations, Finite difference, Image processing, nVidia, OpenCL, Package, Partial differential equations, PDEs, Tesla K20

February 17, 2014 by hgpu

Work-Efficient Parallel GPU Methods for Single-Source Shortest Paths

Andrew Davidson, Sean Baxter, Michael Garland, John D. Owens

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Tags: Algorithms, Computer science, CUDA, Graph theory, nVidia, nVidia GeForce GTX 680, Package

February 15, 2014 by hgpu

Effective Multi-Modal Retrieval based on Stacked Auto-Encoders

Wei Wang, Beng Chin Ooi, Xiaoyan Yang, Dongxiang Zhang, Yueting Zhuang

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Tags: Computer science, CUDA, Information Retrieval, nVidia, nVidia GeForce GTX Titan, Package, Python

February 14, 2014 by hgpu

Multi-Kepler GPU vs. Multi-Intel MIC for spin systems simulations

M. Bernaschi, M. Bisson, F. Salvadore

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Tags: Benchmarking, Computer science, CUDA, Intel Xeon Phi, nVidia, Package, Performance, Physics, Spin Systems, Tesla K20

February 14, 2014 by hgpu

Genetically Improved CUDA C++ Software

W. B. Langdon, M. Harman

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Tags: Computer science, CUDA, Genetic programming, nVidia, nVidia GeForce GTX 295, nVidia GeForce GTX 580, nVidia Quadro NVS 290, Package, Tesla C2050, Tesla K20, Tesla T10

February 11, 2014 by hgpu

Fast 2-D Ultrasound Strain Imaging: The Benefits of Using a GPU

Tim Idzenga, Evghenii Gaburov, Willem Vermin, Jan Menssen, Chris L. de Korte

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Tags: Algorithms, CUDA, Image processing, nVidia, OpenMP, Package, Tesla K20, Ultrasound

February 8, 2014 by hgpu

Parallel implementation of 3D protein structure similarity searches using a GPU and the CUDA

Dariusz Mrozek, Milosz Brozek, Bozena Malysiak-Mrozek

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Tags: Algorithms, Bioinformatics, Biology, CUDA, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 560 Ti, Package, Tesla C1060

February 4, 2014 by hgpu

Fast Acceleration of 2D Wave Propagation Simulations Using Modern Computational Accelerators

Wei Wang, Lifan Xu, John Cavazos, Howie H. Huang, Matthew Kay

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Tags: Computer science, CUDA, Intel Xeon Phi, nVidia, OpenACC, OpenCL, Optimization, Package

February 4, 2014 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

Scheduling data flow program in xkaapi: A new affinity based Algorithm for Heterogeneous Architectures

Extending the Generalized Fermat Prime Number Search Beyond One Million Digits Using GPUs

MICA: A fast short-read aligner that takes full advantage of Intel Many Integrated Core Architecture (MIC)

Fast Hamiltonian Monte Carlo Using GPU Computing

Optimizing Performance of Stencil Code with SPL Conqueror

Work-Efficient Parallel GPU Methods for Single-Source Shortest Paths

Effective Multi-Modal Retrieval based on Stacked Auto-Encoders

Multi-Kepler GPU vs. Multi-Intel MIC for spin systems simulations

Genetically Improved CUDA C++ Software

Fast 2-D Ultrasound Strain Imaging: The Benefits of Using a GPU

Parallel implementation of 3D protein structure similarity searches using a GPU and the CUDA

Fast Acceleration of 2D Wave Propagation Simulations Using Modern Computational Accelerators

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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