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Scalability Analysis of Synchronous Data-Parallel Artificial Neural Network (ANN) Learners

Chang Sun

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Tags: Computer science, Machine learning, Neural networks, nVidia, OpenCL, Tesla P100, Thesis

September 23, 2018 by hgpu

Support for Parallel Scan in OpenMP

Eder Maicol Gomez Zegarra

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Tags: Algorithms, Computer science, CUDA, Filtering, nVidia, OpenCL, OpenMP, Sorting, Thesis

September 23, 2018 by hgpu

SoaAlloc: Accelerating Single-Method Multiple-Objects Applications on GPUs

Matthias Springer

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX Titan X, Programming Languages

September 23, 2018 by hgpu

Characterising Across-Stack Optimisations for Deep Convolutional Neural Networks

Jack Turner, Jose Cano, Valentin Radu, Elliot J. Crowley, Michael O'Boyle, Amos Storkey

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Tags: ARM, Compression, Computer science, Deep learning, Machine learning, Neural networks, OpenCL, OpenMP, Package

September 16, 2018 by hgpu

A deep learning approach to autonomous lunar landing

Ilaria Bloise, Marcello Orlandelli

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Tags: Algorithms, Artificial intelligence, Computer science, Deep learning, LSTM, Machine learning, Neural networks, nVidia, RNN, Tesla K20, Thesis

September 16, 2018 by hgpu

Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization with Medical Applications

Geir Thore Berge, Ole-Christoffer Granmo, Tor Oddbjorn Tveit, Morten Goodwin, Lei Jiao, Bernt Viggo Matheussen

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Tags: CUDA, Deep learning, Keras, Medicine, Neural networks, nVidia, Package, TensorFlow

September 16, 2018 by hgpu

Benchmarking and Optimization of Gradient Boosted Decision Tree Algorithms

Andreea Anghel, Nikolaos Papandreou, Thomas Parnell, Alessandro De Palma, Haralampos Pozidis

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Tags: Algorithms, Bayesian, Benchmarking, Computer science, CUDA, Machine learning, nVidia, nVidia GeForce GTX 1080 Ti

September 16, 2018 by hgpu

ZUCL: A ZYNQ UltraScale+ Framework for OpenCL HLS Applications

Khoa Dang Pham, Anuj Vaishnav, Malte Vesper, Dirk Koch

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Tags: Computer science, FPGA, HLS, OpenCL, Package, Task scheduling

September 16, 2018 by hgpu

Efficient and Scalable k-Means on GPUs

Clemens Lutz, Sebastian Bress, Tilmann Rabl, Steffen Zeuch, Volker Markl

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Tags: Algorithms, Clustering, Computer science, nVidia, nVidia GeForce GTX 1080, OpenCL, Package

September 9, 2018 by hgpu

Developing a New Storage Format and a Warp-Based SpMV Kernel for Configuration Interaction Sparse Matrices on the GPU

Mohammed Mahmoud, Mark Hoffmann, Hassan Reza

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Tags: Algorithms, Computer science, CUDA, nVidia, Sparse matrix, Tesla K20

September 9, 2018 by hgpu

Doctor AI: Interpretable Deep Learning for Modeling Electronic Health Records

Edward Choi

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Tags: CUDA, Deep learning, Medicine, nVidia, nVidia GeForce GTX Titan X, Package, RNN, Tesla K80, Theano, Thesis

September 9, 2018 by hgpu

Using SIMD and SIMT vectorization to evaluate sparse chemical kinetic Jacobian matrices and thermochemical source terms

Nicholas J. Curtis, Kyle E. Niemeyer, Chih-Jen Sung

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Tags: Chemical Physics, Code generation, Computational Physics, Factorization, nVidia, OpenCL, Package, Physics, Tesla C2075, Tesla K40

September 9, 2018 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

Programming

Scalability Analysis of Synchronous Data-Parallel Artificial Neural Network (ANN) Learners

Support for Parallel Scan in OpenMP

SoaAlloc: Accelerating Single-Method Multiple-Objects Applications on GPUs

Characterising Across-Stack Optimisations for Deep Convolutional Neural Networks

A deep learning approach to autonomous lunar landing

Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization with Medical Applications

Benchmarking and Optimization of Gradient Boosted Decision Tree Algorithms

ZUCL: A ZYNQ UltraScale+ Framework for OpenCL HLS Applications

Efficient and Scalable k-Means on GPUs

Developing a New Storage Format and a Warp-Based SpMV Kernel for Configuration Interaction Sparse Matrices on the GPU

Doctor AI: Interpretable Deep Learning for Modeling Electronic Health Records

Using SIMD and SIMT vectorization to evaluate sparse chemical kinetic Jacobian matrices and thermochemical source terms

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