high performance computing on graphics processing units: hgpu.org

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Texture Cache Approximation on GPUs

Mark Sutherland, Joshua San Miguel, Natalie Enright Jerger

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 780, Performance

July 3, 2015 by hgpu

GPU phase-field lattice Boltzmann simulations of growth and motion of a binary alloy dendrite

T. Takaki, R. Rojas, M Ohno, T. Shimokawabe, T. Aoki

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Tags: Alloys, Condensed matter, CUDA, Lattice Boltzmann model, Materials Science, nVidia, Physics, Tesla K20

July 3, 2015 by hgpu

Parallel Sparse Coding for Seafloor Image Analysis

Genlang Chen, Chenggang Lai, Miaoqing Huang

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Tags: CUDA, Image processing, Intel Xeon Phi, Machine learning, nVidia, Tesla K20

July 3, 2015 by hgpu

Buffer overflow vulnerabilities in CUDA: a preliminary analysis

Andrea Miele

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX Titan Black, Security

July 1, 2015 by hgpu

Spectral Ewald Acceleration of Stokesian Dynamics for polydisperse suspensions

Mu Wang, John F. Brady

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Tags: Computational Physics, CUDA, Fluid dynamics, nVidia, nVidia GeForce GTX 680, Physics, Soft Condensed Matter

July 1, 2015 by hgpu

Heterogeneous Network Embedding via Deep Architectures

Shiyu Chang, Wei Han, Jiliang Tang, Guo-Jun Qi, Charu C. Aggarwal, Thomas S. Huang

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Tags: Computer science, CUDA, Data mining, Deep learning, Machine learning, nVidia, Tesla K40

July 1, 2015 by hgpu

AccFFT: A library for distributed-memory FFT on CPU and GPU architectures

Amir Gholami, Judith Hill, Dhairya Malhotra, George Biros

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Tags: Algorithms, Computer science, CUDA, FFT, MPI, nVidia, OpemMP, Package, Tesla K20

June 30, 2015 by hgpu

Intra-Application Data-Communication Characterization

Imran Ashraf, Vlad-Mihai Sima, Koen Bertels

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GT 640

June 30, 2015 by hgpu

Compiling High Performance Recursive Filters

Gaurav Chaurasia, Jonathan Ragan-Kelley, Sylvain Paris, George Drettakis, Fredo Durand

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Tags: Algorithms, CUDA, Filtering, Image processing, nVidia, nVidia GeForce GTX Titan

June 30, 2015 by hgpu

Improving the Mapping of Smith-Waterman Sequence Database Searches onto CUDA-Enabled GPUs

Liang-Tsung Huang, Chao-Chin Wu, Lien-Fu Lai, Yun-Ju Li

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Tags: Bioinformatics, Biology, CUDA, Databases, nVidia, Sequence alignment, Smith-Waterman algorithm, Tesla C1060, Tesla K20

June 30, 2015 by hgpu

Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation

Marijn F. Stollenga, Wonmin Byeon, Marcus Liwicki, Juergen Schmidhuber

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Tags: Computer science, CUDA, Deep learning, Image processing, Medicine, Neural networks, nVidia, nVidia GeForce GTX Titan X

June 26, 2015 by hgpu

Concurrent Solutions to Linear Systems using Hybrid CPU/GPU Nodes

Oluwapelumi Adenikinju, Julian Gilyard, Joshua Massey, Thomas Stitt

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Tags: Computer science, CUDA, Linear Algebra, nVidia, OpenGL, OpenMP

June 26, 2015 by hgpu

Tutoring LLM into a Better CUDA Optimizer

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Kernel Library for LLM Serving

Compiler and Runtime Systems for Generative AI Models

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

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high performance computing on graphics processing units: hgpu.org

Texture Cache Approximation on GPUs

GPU phase-field lattice Boltzmann simulations of growth and motion of a binary alloy dendrite

Parallel Sparse Coding for Seafloor Image Analysis

Buffer overflow vulnerabilities in CUDA: a preliminary analysis

Spectral Ewald Acceleration of Stokesian Dynamics for polydisperse suspensions

Heterogeneous Network Embedding via Deep Architectures

AccFFT: A library for distributed-memory FFT on CPU and GPU architectures

Intra-Application Data-Communication Characterization

Compiling High Performance Recursive Filters

Improving the Mapping of Smith-Waterman Sequence Database Searches onto CUDA-Enabled GPUs

Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation

Concurrent Solutions to Linear Systems using Hybrid CPU/GPU Nodes

Recent source codes

OpScanner

Atlas CLI: Machine Learning (ML) Lifecycle & Transparency Manager

transformers_tvm: Implementation of Encoder Decoder transformer on TVM

INT v.s. FP: A framework to compare low-bit integer and float-point formats

AutoDock-GPU: AutoDock for GPUs and other accelerators

NCCLX: collective communication framework

Tutoring LLM into a Better CUDA Optimizer

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Kernel Library for LLM Serving

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

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