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GPGPU Accelerated Deep Object Classification on a Heterogeneous Mobile Platform

Syed Tahir Hussain Rizvi, Gianpiero Cabodi, Denis Patti, Gianluca Francini

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Tags: CNN, Computer science, CUDA, Deep learning, Heterogeneous systems, Image processing, Matrix multiplication, Neural networks, nVidia, nVidia Jetson TK1

December 10, 2016 by hgpu

ESE: Efficient Speech Recognition Engine with Compressed LSTM on FPGA

Song Han, Junlong Kang, Huizi Mao, Yiming Hu, Xin Li, Yubin Li, Dongliang Xie, Hong Luo, Song Yao, Yu Wang, Huazhong Yang, William J. Dally

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Tags: Computer science, CUDA, Deep learning, FPGA, LSTM, Machine learning, nVidia, nVidia GeForce GTX Titan X, Speech recognition

December 6, 2016 by hgpu

Should I use TensorFlow?

Martin Schrimpf

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Tags: Computer science, CUDA, Deep learning, Machine learning, nVidia, Performance, Python, TensorFlow

December 3, 2016 by hgpu

Parallelizing Word2Vec in Multi-Core and Many-Core Architectures

Shihao Ji, Nadathur Satish, Sheng Li, Pradeep Dubey

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Tags: Computer science, CUDA, Deep learning, Intel Xeon Phi, NLP, nVidia, nVidia GeForce GTX Titan X, Performance, Tesla K40

November 30, 2016 by hgpu

Fast and Energy-Efficient CNN Inference on IoT Devices

Mohammad Motamedi, Daniel Fong, Soheil Ghiasi

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Tags: CNN, Computer science, Deep learning, Java, Machine learning, Neural networks, Package, RenderScript, SoC

November 25, 2016 by hgpu

dMath: Distributed Linear Algebra for DL

Steven Eliuk, Cameron Upright, Hars Vardhan, Stephen Walsh, Trevor Gale

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Tags: Algorithms, Computer science, CUDA, Deep learning, Linear Algebra, Matrix multiplication, MPI, Neural networks, nVidia, Tesla K80

November 25, 2016 by hgpu

A Metaprogramming and Autotuning Framework for Deploying Deep Learning Applications

Matthew W. Moskewicz, Ali Jannesari, Kurt Keutzer

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Tags: AMD R9 Nano, ARM, ATI, Code generation, Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX Titan X, OpenCL, Package

November 23, 2016 by hgpu

Deep Tensor Convolution on Multicores

David Budden, Alexander Matveev, Shibani Santurkar, Shraman Ray Chaudhuri, Nir Shavit

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX Titan X

November 23, 2016 by hgpu

GA3C: GPU-based A3C for Deep Reinforcement Learning

Mohammad Babaeizadeh, Iuri Frosio, Stephen Tyree, Jason Clemons, Jan Kautz

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX Titan X, TensorFlow

November 23, 2016 by hgpu

SC-DCNN: Highly-Scalable Deep Convolutional Neural Network using Stochastic Computing

Ao Ren, Ji Li, Zhe Li, Caiwen Ding, Xuehai Qian, Qinru Qiu, Bo Yuan, Yanzhi Wang

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Tags: ASIC, CNN, Computer science, Deep learning, FPGA, Neural networks, nVidia, Tesla C2075

November 22, 2016 by hgpu

How to scale distributed deep learning?

Peter H. Jin, Qiaochu Yuan, Forrest Iandola, Kurt Keutzer

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Tags: Computer science, CUDA, Deep learning, GPU cluster, nVidia, Performance, Tesla K20, Tesla K80

November 19, 2016 by hgpu

Efficient Communications in Training Large Scale Neural Networks

Linnan Wang, Wei Wu, George Bosilca, Richard Vuduc, Zenglin Xu

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Tags: Caffe, Computer science, CUDA, Deep learning, MPI, Neural networks, nVidia, OpenMPI, Performance

November 16, 2016 by hgpu

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

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high performance computing on graphics processing units: hgpu.org

GPGPU Accelerated Deep Object Classification on a Heterogeneous Mobile Platform

ESE: Efficient Speech Recognition Engine with Compressed LSTM on FPGA

Should I use TensorFlow?

Parallelizing Word2Vec in Multi-Core and Many-Core Architectures

Fast and Energy-Efficient CNN Inference on IoT Devices

dMath: Distributed Linear Algebra for DL

A Metaprogramming and Autotuning Framework for Deploying Deep Learning Applications

Deep Tensor Convolution on Multicores

GA3C: GPU-based A3C for Deep Reinforcement Learning

SC-DCNN: Highly-Scalable Deep Convolutional Neural Network using Stochastic Computing

How to scale distributed deep learning?

Efficient Communications in Training Large Scale Neural Networks

Recent source codes

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

LLaMA-Inference-Bench

MATAR

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

MaC: Mamba CPU performance predictor

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

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