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Improving the Performance of Fully Connected Neural Networks by Out-of-Place Matrix Transpose

Shaohuai Shi, Pengfei Xu, Xiaowen Chu

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Tags: Algorithms, BLAS, Caffe, Computer science, CUBLAS, CUDA, Deep learning, Linear Algebra, Matrix multiplication, Neural networks, nVidia, nVidia GeForce GTX 1080, Package, Performance

February 14, 2017 by hgpu

Accelerating Binarized Neural Networks: Comparison of FPGA, CPU, GPU, and ASIC

Eriko Nurvitadhi, David Sheffield, Jaewoong Sim, Asit Mishra, Ganesh Venkatesh, Debbie Marr

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Tags: Algorithms, ASIC, Computer science, CUDA, Deep learning, FPGA, Neural networks, nVidia, nVidia GeForce GTX Titan X, nVidia Tegra TX1, Performance

February 14, 2017 by hgpu

Development of JavaScript-based deep learning platform and application to distributed training

Masatoshi Hidaka, Ken Miura, Tatsuya Harada

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Tags: Computer science, Deep learning, Java, Neural networks, nVidia, OpenCL, Package, Tesla K80, WebCL

February 10, 2017 by hgpu

Recurrent Neural Networks for anomaly detection in the Post-Mortem time series of LHC superconducting magnets

Maciej Wielgosz, Andrzej Skoczen, Matej Mertik

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Tags: CUDA, Deep learning, Instrumentation and Detectors, Keras, LSTM, Machine learning, Neural networks, nVidia, Physics, RNN, Tesla K80, Theano

February 7, 2017 by hgpu

Critical Comparison of the Classification Ability of Deep Convolutional Neural Network Frameworks with Support Vector Machine Techniques in the Image Classification Process

Robert T. Kelly

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Tags: Computer science, Deep learning, Image processing, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 970, Theano, Thesis, Torch

February 5, 2017 by hgpu

Deep Convolutional Network evaluation on the Intel Xeon Phi: Where Subword Parallelism meets Many-Core

Gaurav Raina

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Tags: Computer science, Computer vision, Deep learning, Intel Xeon Phi, Neural networks, OpenMP, Thesis

January 26, 2017 by hgpu

Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, Jeff Dean

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Tags: Algorithms, Computer science, CUDA, Deep learning, GPU cluster, Machine learning, Neural networks, nVidia, TensorFlow, Tesla K40

January 26, 2017 by hgpu

DeepBach: a Steerable Model for Bach chorales generation

Gaetan Hadjeres, Francois Pachet

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Tags: Artificial intelligence, Computer science, CUDA, Deep learning, Keras, nVidia, nVidia GeForce GTX 980 Ti, Package

January 23, 2017 by hgpu

Deep Learning for Computational Chemistry

Garrett B. Goh, Nathan O. Hodas, Abhinav Vishnu

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Tags: Algorithms, Chemistry, Computational chemistry, Computer science, Deep learning, Machine learning, Neural networks, Review

January 19, 2017 by hgpu

OpenNMT: Open-Source Toolkit for Neural Machine Translation

Guillaume Klein, Yoon Kim, Yuntian Deng, Jean Senellart, Alexander M. Rush

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Tags: Computer science, CUDA, Deep learning, Machine learning, NLP, nVidia, nVidia GeForce GTX 1080, Package, Python, Torch

January 19, 2017 by hgpu

An OpenCL(TM) Deep Learning Accelerator on Arria 10

Utku Aydonat, Shane O'Connell, Davor Capalija, Andrew C. Ling, Gordon R. Chiu

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Tags: CNN, Computer science, Computer vision, Deep learning, FPGA, OpenCL, Performance

January 16, 2017 by hgpu

DeepDSL: A Compilation-based Domain-Specific Language for Deep Learning

Tian Zhao, Xiaobing Huang, Yu Cao

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Tags: Computer science, CUDA, Deep learning, DSL, Java, Machine learning, nVidia, Package, Programming Languages, Scala, Tesla K40

January 10, 2017 by hgpu

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

Advances in Semantic Patching for HPC-oriented Refactorings with Coccinelle

DuoReduce: MLIR's benchmark

Hardware-Assisted Software Testing and Debugging for Heterogeneous Computing

Shamrock: Multi-GPU hydrodynamics for astrophysics

The Shamrock code: I- Smoothed Particle Hydrodynamics on GPUs

LLMPerf: GPU Performance Modeling meets Large Language Models

Hercules: A Compiler for Productive Programming of Heterogeneous Systems

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high performance computing on graphics processing units: hgpu.org

Improving the Performance of Fully Connected Neural Networks by Out-of-Place Matrix Transpose

Accelerating Binarized Neural Networks: Comparison of FPGA, CPU, GPU, and ASIC

Development of JavaScript-based deep learning platform and application to distributed training

Recurrent Neural Networks for anomaly detection in the Post-Mortem time series of LHC superconducting magnets

Critical Comparison of the Classification Ability of Deep Convolutional Neural Network Frameworks with Support Vector Machine Techniques in the Image Classification Process

Deep Convolutional Network evaluation on the Intel Xeon Phi: Where Subword Parallelism meets Many-Core

Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

DeepBach: a Steerable Model for Bach chorales generation

Deep Learning for Computational Chemistry

OpenNMT: Open-Source Toolkit for Neural Machine Translation

An OpenCL(TM) Deep Learning Accelerator on Arria 10

DeepDSL: A Compilation-based Domain-Specific Language for Deep Learning

Recent source codes

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

DuoReduce: MLIR's benchmark

Shamrock: Multi-GPU hydrodynamics for astrophysics

LLMPerf: GPU Performance Modeling meets Large Language Models

Hercules: A Compiler for Productive Programming of Heterogeneous Systems

Celerity Runtime: High-level C++ for Accelerator Clusters

wgpy: WebGL accelerated numpy-compatible array library for web browser

Microbenchmarking OpenMP target offload with Catch2

SUperman: Highly Efficient Permanent Computation Library

TransCL: An Automatic CUDA-to-OpenCL Programs Transformation Framework

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