high performance computing on graphics processing units: hgpu.org

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Parallel Wavelet Schemes for Images

David Barina, Michal Kula, Pavel Zemcik

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Tags: AMD Radeon R9 290X, AMD Radeon R9 Fury, ATI, Compression, Discrete Wavelet Transform, Image processing, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX Titan X, OpenCL, Package

May 7, 2016 by hgpu

TheanoLM – An Extensible Toolkit for Neural Network Language Modeling

Seppo Enarvi, Mikko Kurimo

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Tags: Computer science, CUDA, Deep learning, Machine learning, Neural networks, NLP, nVidia, Package, Python, RNN, Speech recognition, Theano

May 7, 2016 by hgpu

TrimZero: A Torch Recurrent Module for Efficient Natural Language Processing

Jin-Hwa Kim, Jeonghee Kim, Jung-Woo Ha, Byoung-Tak Zhang

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Tags: Computer science, CUDA, Deep learning, Lua, Machine learning, Neural networks, NLP, nVidia, Package, RNN, Torch

May 3, 2016 by hgpu

Array Program Transformation with Loo.py by Example: High-Order Finite Elements

Andreas Klockner, Lucas C. Wilcox, T. Warburton

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Tags: Algorithms, AMD Radeon R9 Fury, ATI, Code generation, Computer science, Fortran, Numerical Analysis, nVidia, nVidia GeForce GTX Titan X, OpenCL, Package, Performance, Prefetch, Programming Languages, Python, Tesla K40

April 29, 2016 by hgpu

Automatic Parallelization: Executing Sequential Programs on a Task-Based Parallel Runtime

Alcides Fonseca, Bruno Cabral, Joao Rafael, Ivo Correia

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Tags: Computer science, Java, OpenCL, Package, Programming Languages

April 29, 2016 by hgpu

Adaptive GPU Array Layout Auto-Tuning

Nicolas Weber, Michael Goesele

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Tags: Computer science, CUDA, Linear Algbera, nVidia, nVidia GeForce GTX Titan X, Package, Performance, Tesla K20

April 29, 2016 by hgpu

Investigating performance portability of a highly scalable particle-in-cell simulation code on various multi-core architectures

Benjamin Worpitz

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Tags: Computer science, CUDA, Intel Xeon Phi, OpenMP, Package, Particle-in-cell methods, Performance, performance portability, Tesla K20, Tesla K80, Thesis

April 26, 2016 by hgpu

To Co-Run, or Not To Co-Run: A Performance Study on Integrated Architectures

Feng Zhang, Jidong Zhai, Wenguang Chen, Bingsheng He, Shuhao Zhang

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Tags: Benchmarking, Computer science, OpenCL, Package

April 26, 2016 by hgpu

CMA-ES for Hyperparameter Optimization of Deep Neural Networks

Ilya Loshchilov, Frank Hutter

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Tags: Algorithms, Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX Titan, Package, Python

April 26, 2016 by hgpu

LightScan: Faster Scan Primitive on CUDA Compatible Manycore Processors

Yongchao Liu, Srinivas Aluru

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Tags: Algorithms, Computer science, CUDA, Intel Xeon Phi, nVidia, Package, Performance, PTX, Tesla K40

April 19, 2016 by hgpu

Efficiency of general Krylov methods on GPUs – An experimental study

Hartwig Anzt, Jack Dongarra, Moritz Kreutzer, Gerhard Wellein, Martin Kohler

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Tags: Computer science, CUDA, Intel Xeon Phi, Linear Algebra, nVidia, OpenCL, Package, Sparse matrix, Tesla K40

April 16, 2016 by hgpu

GPU-FV: Realtime Fisher Vector and Its Applications in Video Monitoring

Wenying Ma, Liangliang Cao, Lei Yu, Guoping Long, Yucheng Li

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Tags: Computer science, Computer vision, CUDA, Deep learning, nVidia, Package, Tesla K40

April 14, 2016 by hgpu

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Efficient deep learning inference on end devices

Ouroboros: Virtualized Queues for dynamic memory management

Dynamic Memory Management on GPUs with SYCL

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high performance computing on graphics processing units: hgpu.org

Packages

Parallel Wavelet Schemes for Images

TheanoLM – An Extensible Toolkit for Neural Network Language Modeling

TrimZero: A Torch Recurrent Module for Efficient Natural Language Processing

Array Program Transformation with Loo.py by Example: High-Order Finite Elements

Automatic Parallelization: Executing Sequential Programs on a Task-Based Parallel Runtime

Adaptive GPU Array Layout Auto-Tuning

Investigating performance portability of a highly scalable particle-in-cell simulation code on various multi-core architectures

To Co-Run, or Not To Co-Run: A Performance Study on Integrated Architectures

CMA-ES for Hyperparameter Optimization of Deep Neural Networks

LightScan: Faster Scan Primitive on CUDA Compatible Manycore Processors

Efficiency of general Krylov methods on GPUs – An experimental study

GPU-FV: Realtime Fisher Vector and Its Applications in Video Monitoring

Recent source codes

XaaS containers

microSYCL: SYCL micro-benchmarks repository

SYCL Container

CASS: Cuda-Amd aSSembly

Cluser of smartphones for edge computing application using TensorFlow

CFAL-bench

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Ouroboros: Virtualized Queues for dynamic memory management

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