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Distributed Deep Learning Strategies For Automatic Speech Recognition

Wei Zhang, Xiaodong Cui, Ulrich Finkler, Brian Kingsbury, George Saon, David Kung, Michael Picheny

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Tags: Computer science, CUDA, Deep learning, LSTM, Machine learning, nVidia, OpenMPI, Speech recognition, Tesla P100

April 14, 2019 by hgpu

On the Representation of Partially Specified Implementations and its Application to the Optimization of Linear Algebra Kernels on GPU

Ulysse Beaugnon, Basile Clément, Nicolas Tollenaere, Albert Cohen

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Tags: Analytical model, Computer science, CUDA, Linear Algebra, nVidia, nVidia Quadro K4000, Programming Languages

April 14, 2019 by hgpu

High Performance Monte Carlo Simulation of Ising Model on TPU Clusters

Kun Yang, Yi-Fan Chen, Georgios Roumpos, Chris Colby, John Anderson

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Tags: Cloud, CUDA, Ising model, Machine learning, Monte Carlo simulation, Neural networks, nVidia, Package, Physics, Tesla V100, TPU

April 7, 2019 by hgpu

fairseq: A Fast, Extensible Toolkit for Sequence Modeling

Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, Michael Auli

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Tags: Computer science, CUDA, NLP, nVidia, Package, PyTorch, Video

April 7, 2019 by hgpu

Methods for Accelerating Machine Learning in High Performance Computing

Robert Lim

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, Machine learning, nVidia, Tesla P100, Thesis

March 31, 2019 by hgpu

Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey

Giang Nguyen, Stefan Dlugolinsky, Martin Bobak, Viet Tran, Alvaro Lopez Garcia, Ignacio Heredia, Peter Malik, Ladislav Hluchy

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Tags: Algorithms, Artificial intelligence, Computer science, CUDA, Data mining, Deep learning, Machine learning, nVidia, OpenCL

March 31, 2019 by hgpu

Hybrid CPU-GPU execution support in the skeleton programming framework SkePU

Tomas Oehberg, August Ernstsson, Christoph Kessler

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

March 31, 2019 by hgpu

swCaffe: a Parallel Framework for Accelerating Deep Learning Applications on Sunway TaihuLight

Jiarui Fang, Liandeng Li, Haohuan Fu, Jinlei Jiang, Wenlai Zhao, Conghui He, Xin You, Guangwen Yang

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Tags: Computer science, CUDA, Deep learning, Heterogeneous systems, Neural networks, nVidia, OpenMPI, Package, Tesla K40

March 24, 2019 by hgpu

Dissecting the NVidia Turing T4 GPU via Microbenchmarking

Zhe Jia, Marco Maggioni, Jeffrey Smith, Daniele Paolo Scarpazza

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Tags: Benchmarking, Computer science, CUDA, Deep learning, nVidia, Performance, PTX, Tesla P100, Tesla V100

March 24, 2019 by hgpu

Novel Data-Partitioning Algorithms for Performance and Energy Optimization of Data-Parallel Applications on Modern Heterogeneous HPC Platforms

Hamidreza Khaleghzadeh

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Tags: Computer science, CUDA, FPGA, Heterogeneous systems, Matrix multiplication, nVidia, OpenCL, Package, Thesis

March 17, 2019 by hgpu

Analyzing GPU Tensor Core Potential for Fast Reductions

Roberto Carrasco, Raimundo Vega, Cristóbal A. Navarro

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Tags: Computer science, CUDA, Deep learning, nVidia, Performance, Tesla V100

March 17, 2019 by hgpu

TensorFlow Doing HPC

Steven W. D. Chien, Stefano Markidis, Vyacheslav Olshevsky, Yaroslav Bulatov, Erwin Laure, Jeffrey S. Vetter

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Tags: Benchmarking, Computer science, CUDA, Deep learning, FFT, Heterogeneous systems, HPC, Machine learning, nVidia, nVidia Quadro K420, OpenMPI, Package, Performance, Python, TensorFlow, Tesla K80, Tesla V100

March 17, 2019 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

MSCCL++: A GPU-driven communication stack for scalable AI applications

MSCCL++: Rethinking GPU Communication Abstractions for Cutting-edge AI Applications

Benchmark compute shader of Unity against InteropUnityCUDA

InteropUnityCUDA: A Tool for Interoperability Between Unity and CUDA

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high performance computing on graphics processing units: hgpu.org

Distributed Deep Learning Strategies For Automatic Speech Recognition

On the Representation of Partially Specified Implementations and its Application to the Optimization of Linear Algebra Kernels on GPU

fairseq: A Fast, Extensible Toolkit for Sequence Modeling

Methods for Accelerating Machine Learning in High Performance Computing

Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey

Hybrid CPU-GPU execution support in the skeleton programming framework SkePU

swCaffe: a Parallel Framework for Accelerating Deep Learning Applications on Sunway TaihuLight

Dissecting the NVidia Turing T4 GPU via Microbenchmarking

Novel Data-Partitioning Algorithms for Performance and Energy Optimization of Data-Parallel Applications on Modern Heterogeneous HPC Platforms

Analyzing GPU Tensor Core Potential for Fast Reductions

TensorFlow Doing HPC

Recent source codes

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

MSCCL++: A GPU-driven communication stack for scalable AI applications

Benchmark compute shader of Unity against InteropUnityCUDA

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