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Accelerating Deep Convolutional Neural Networks Using Specialized Hardware

Kalin Ovtcharov, Olatunji Ruwase, Joo-Young Kim, Jeremy Fowers, Karin Strauss, Eric S. Chung

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Tags: Computer science, FPGA, Machine learning, Neural networks, Speech recognition

February 27, 2015 by hgpu

Large-Scale Deep Learning on the YFCC100M Dataset

Karl Ni, Roger Pearce, Kofi Boakye, Brian Van Essen, Damian Borth, Barry Chen, Eric Wang

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Tags: Computer science, Computer vision, CUDA, Deep learning, Machine learning, Neural networks, nVidia, Tesla M2050

February 13, 2015 by hgpu

Scaling Recurrent Neural Network Language Models

Will Williams, Niranjani Prasad, David Mrva, Tom Ash, Tony Robinson

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

February 3, 2015 by hgpu

On Vectorization of Deep Convolutional Neural Networks for Vision Tasks

Jimmy SJ. Ren, Li Xu

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Tags: CNN, Computer science, Computer vision, CUDA, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 780 Ti, Package

January 30, 2015 by hgpu

Revisit Long Short-Term Memory: An Optimization Perspective

Qi Lyu, Jun Zhu

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Tags: Computer science, CUDA, Machine learning, MapReduce, Neural networks, nVidia, nVidia GeForce GTX 770

January 23, 2015 by hgpu

Convolutional Neural Networks for Human Activity Recognition using Mobile Sensors

Ming Zeng, Le T. Nguyen, Bo Yu, Ole J. Mengshoel, Jiang Zhu, Pang Wu, Joy Zhang

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Tags: CNN, CUDA, Image recognition, Neural networks, nVidia, Sensing, Speech recognition, Tesla K20

January 13, 2015 by hgpu

Image Super-Resolution Using Deep Convolutional Networks

Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang

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Tags: CNN, Computer science, CUDA, Machine learning, Neural networks, nVidia, Package

January 10, 2015 by hgpu

Cardiac Dysrhythmia Detection with GPU-Accelerated Neural Networks

Albert Haque

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Tags: CUDA, Machine learning, Medicine, Neural networks, nVidia, nVidia GeForce GTX 750 Ti, Package

January 8, 2015 by hgpu

Disjunctive Normal Networks

Mehdi Sajjadi, Mojtaba Seyedhosseini, Tolga Tasdizen

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Tags: Computer science, Computer vision, CUDA, Machine learning, Neural and Evolutionary Computing, Neural networks, nVidia

January 2, 2015 by hgpu

Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs

Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, Alan L. Yuille

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Tags: Algorithms, CNN, Computer science, Computer vision, CUDA, Neural networks, nVidia, nVidia GeForce GTX Titan

December 26, 2014 by hgpu

Fast Convolutional Nets With fbfft: A GPU Performance Evaluation

Nicolas Vasilache, Jeff Johnson, Michael Mathieu, Soumith Chintala, Serkan Piantino, Yann LeCun

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Tags: Algorithms, CNN, Computer science, CUDA, FFT, Machine learning, Neural and Evolutionary Computing, Neural networks, nVidia, Package, Tesla K40

December 26, 2014 by hgpu

Highly Efficient Forward and Backward Propagation of Convolutional Neural Networks for Pixelwise Classification

Hongsheng Li, Rui Zhao, Xiaogang Wang

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Tags: Algorithms, CNN, Computer science, Computer vision, CUDA, Machine learning, Neural networks, nVidia, Tesla K40

December 16, 2014 by hgpu

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

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

DeepCompile: A Compiler-Driven Approach to Optimizing Distributed Deep Learning Training

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

GigaAPI for GPU Parallelization

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

Advances in Semantic Patching for HPC-oriented Refactorings with Coccinelle

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high performance computing on graphics processing units: hgpu.org

Accelerating Deep Convolutional Neural Networks Using Specialized Hardware

Large-Scale Deep Learning on the YFCC100M Dataset

Scaling Recurrent Neural Network Language Models

On Vectorization of Deep Convolutional Neural Networks for Vision Tasks

Revisit Long Short-Term Memory: An Optimization Perspective

Convolutional Neural Networks for Human Activity Recognition using Mobile Sensors

Image Super-Resolution Using Deep Convolutional Networks

Cardiac Dysrhythmia Detection with GPU-Accelerated Neural Networks

Disjunctive Normal Networks

Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs

Fast Convolutional Nets With fbfft: A GPU Performance Evaluation

Highly Efficient Forward and Backward Propagation of Convolutional Neural Networks for Pixelwise Classification

Recent source codes

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

Benchmark compute shader of Unity against InteropUnityCUDA

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

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

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