high performance computing on graphics processing units: hgpu.org

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Learning a Metric Embedding for Face Recognition using the Multibatch Method

Oren Tadmor, Yonatan Wexler, Tal Rosenwein, Shai Shalev-Shwartz, Amnon Shashua

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Tags: Algorithms, ARM, Computer science, Deep learning, Neural networks, nVidia, nVidia GeForce GTX Titan X, Performance

May 26, 2016 by hgpu

Implementing Deep Neural Networks for Financial Market Prediction on the Intel Xeon Phi

Matthew Dixon, Diego Klabjan, Jin Hoon Bang

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Tags: Algorithms, CNN, Computer science, Deep learning, Finance, Intel Xeon Phi, Neural networks

May 26, 2016 by hgpu

Ristretto: Hardware-Oriented Approximation of Convolutional Neural Networks

Philipp Gysel

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Tags: Caffe, Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, Package, Tesla K40, Thesis

May 23, 2016 by hgpu

Deep Roots: Improving CNN Efficiency with Hierarchical Filter Groups

Yani Ioannou, Duncan Robertson, Roberto Cipolla, Antonio Criminisi

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Tags: Computer science, CUDA, Neural and Evolutionary Computing, Neural networks, nVidia, nVidia GeForce GTX Titan Z

May 23, 2016 by hgpu

DeepLearningKit – an GPU Optimized Deep Learning Framework for Apple’s iOS, OS X and tvOS developed in Metal and Swift

Amund Tveit, Torbjorn Morland, Thomas Brox Rost

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Tags: Computer science, Deep learning, Neural networks, OpenCL, Package, Video, Vulkan

May 17, 2016 by hgpu

An End-to-End System for Unconstrained Face Verifcation with Deep Convolutional Neural Networks

Jun-Cheng Chen, Rajeev Ranjan, Swami Sankaranarayanan, Amit Kumar, Ching-Hui Chen, Vishal M. Patel, Carlos D. Castillo, Rama Chellappa

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

May 11, 2016 by hgpu

LightNet: A Versatile, Standalone Matlab-based Environment for Deep Learning

Chengxi Ye, Chen Zhao, Yezhou Yang, Cornelia Fermuller, Yiannis Aloimonos

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Tags: Computer science, Computer vision, Deep learning, Matlab, Neural networks, nVidia, nVidia GeForce GTX Titan X, Package

May 11, 2016 by hgpu

Training Neural Networks Without Gradients: A Scalable ADMM Approach

Gavin Taylor, Ryan Burmeister, Zheng Xu, Bharat Singh, Ankit Patel, Tom Goldstein

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Tags: Computer science, CUDA, Machine learning, MPI, Neural networks, nVidia, Tesla K40, Torch

May 9, 2016 by hgpu

Going Deeper with Embedded FPGA Platform for Convolutional Neural Network

Jiantao Qiu, Jie Wang, Song Yao, Kaiyuan Guo, Boxun Li, Erjin Zhou, Jincheng Yu, Tianqi Tang, Ningyi Xu, Sen Song, Yu Wang, Huazhong Yang

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Tags: Caffe, Computer science, Computer vision, CUDA, Deep learning, FPGA, Neural networks, nVidia, nVidia Jetson TK1, Tesla K40

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

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

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

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high performance computing on graphics processing units: hgpu.org

Learning a Metric Embedding for Face Recognition using the Multibatch Method

Implementing Deep Neural Networks for Financial Market Prediction on the Intel Xeon Phi

Ristretto: Hardware-Oriented Approximation of Convolutional Neural Networks

Deep Roots: Improving CNN Efficiency with Hierarchical Filter Groups

DeepLearningKit – an GPU Optimized Deep Learning Framework for Apple’s iOS, OS X and tvOS developed in Metal and Swift

An End-to-End System for Unconstrained Face Verifcation with Deep Convolutional Neural Networks

LightNet: A Versatile, Standalone Matlab-based Environment for Deep Learning

Training Neural Networks Without Gradients: A Scalable ADMM Approach

Going Deeper with Embedded FPGA Platform for Convolutional Neural Network

TheanoLM – An Extensible Toolkit for Neural Network Language Modeling

TrimZero: A Torch Recurrent Module for Efficient Natural Language Processing

CMA-ES for Hyperparameter Optimization of Deep Neural Networks

Recent source codes

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

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