high performance computing on graphics processing units: hgpu.org

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Parallel LZ77 Decoding using a GPU

Emmanuel Morfiadakis

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Tags: Algorithms, Compression, Computer science, CUDA, nVidia, nVidia GeForce GTX 840 M, Thesis

September 23, 2018 by hgpu

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

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

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

Optimizing Communication for Clusters of GPUs

Michael Wayne LeBeane

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Tags: Computer science, GPU cluster, Memory model, Thesis

September 2, 2018 by hgpu

Optimizing Web Virtual Reality

Rabimba Karanjai

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Tags: Benchmarking, Computer science, Java, OpenGL, Rendering, Thesis, Virtual reality, WebGL

August 26, 2018 by hgpu

Auto-tuning Hybrid CPU-GPU Execution of Algorithmic Skeletons in SkePU

Tomas Ohberg

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Tags: Computer science, CUDA, Heterogeneous systems, Hybrid computing, nVidia, OpenCL, Tesla K20, Thesis

August 26, 2018 by hgpu

A Qualitative Comparison Study Between Common GPGPU Frameworks

Adam Soderstrom

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Tags: Computer science, CUDA, DirectCompute, DirectX, N-body simulation, nVidia, nVidia GeForce GTX 1050, OpenCL, Thesis

August 26, 2018 by hgpu

Elementary functions: towards automatically generated, efficient, and vectorizable implementations

Hugues De Lassus Saint-Genies

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Tags: Algorithms, Code generation, Computer science, Elementary functions, Performance, Thesis

July 28, 2018 by hgpu

Optimization of OpenCL applications on FPGA

Albert Navarro Torrento

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Tags: Computer science, FPGA, OpenCL, Optimization, Thesis

July 28, 2018 by hgpu

Cross-Compiling Shading Languages

Lukas Hermanns

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Tags: 3D Graphics and Realism, Algorithms, DirectX, GLSL, HLSL, OpenGL, Real-time graphics, Rendering, Thesis

July 15, 2018 by hgpu

TVM: Open deep learning compiler stack for cpu, gpu and specialized accelerators

TVM: An Automated End-to-End Optimizing Compiler for Deep Learning

MyCaffe: A complete deep learning platform written almost entirely in C# for Windows developers

MyCaffe: A Complete C# Re-Write of Caffe with Reinforcement Learning

Characterising Across-Stack Optimisations for Deep Convolutional Neural Networks

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

ZUCL: A ZYNQ UltraScale+ Framework for OpenCL HLS Applications

CL-kmeans: Efficient k-Means in OpenCL

Efficient and Scalable k-Means on GPUs

Doctor AI: Interpretable Deep Learning for Modeling Electronic Health Records

pyJac: a Python package that generates source code used to analytically calculate chemical kinetics Jacobian matrices

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

CUTLASS: CUDA Templates for Linear Algebra Subroutines

Implementing Strassen’s Algorithm with CUTLASS on NVIDIA Volta GPUs

Integer sorting on multicore

A study of integer sorting on multicores

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2019

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13-15

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10-12

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2018

December

13-15

Wellington, New Zealand

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high performance computing on graphics processing units: hgpu.org

Theses

Parallel LZ77 Decoding using a GPU

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

Support for Parallel Scan in OpenMP

A deep learning approach to autonomous lunar landing

Doctor AI: Interpretable Deep Learning for Modeling Electronic Health Records

Optimizing Communication for Clusters of GPUs

Optimizing Web Virtual Reality

Auto-tuning Hybrid CPU-GPU Execution of Algorithmic Skeletons in SkePU

A Qualitative Comparison Study Between Common GPGPU Frameworks

Elementary functions: towards automatically generated, efficient, and vectorizable implementations

Optimization of OpenCL applications on FPGA

Cross-Compiling Shading Languages

Recent source codes

TVM: Open deep learning compiler stack for cpu, gpu and specialized accelerators

MyCaffe: A complete deep learning platform written almost entirely in C# for Windows developers

Characterising Across-Stack Optimisations for Deep Convolutional Neural Networks

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

ZUCL: A ZYNQ UltraScale+ Framework for OpenCL HLS Applications

CL-kmeans: Efficient k-Means in OpenCL

Doctor AI: Interpretable Deep Learning for Modeling Electronic Health Records

pyJac: a Python package that generates source code used to analytically calculate chemical kinetics Jacobian matrices

CUTLASS: CUDA Templates for Linear Algebra Subroutines

Integer sorting on multicore

Most viewed papers (last 30 days)

Featured events

International Conference on Image, Video and Signal Processing (IVSP), 2019

International Joint Conference on Signals, Systems and Computers (CSSC), 2018

International Joint Conference on Robotics, Automation and Mechatronics (JCRAM), 2018

International Joint Conference on Robotics and Artificial Intelligence (JCRAI), 2018

International Joint Conference on Computer Vision and Pattern Recognition (CCVPR), 2018