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MIOpen: An Open Source Library For Deep Learning Primitives

Jehandad Khan, Paul Fultz, Artem Tamazov, Daniel Lowell, Chao Liu, Michael Melesse, Murali Nandhimandalam, Kamil Nasyrov, Ilya Perminov, Tejash Shah, Vasilii Filippov, Jing Zhang, Jing Zhou, Bragadeesh Natarajan, Mayank Daga

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

October 6, 2019 by hgpu

Exascale Deep Learning for Scientific Inverse Problems

Nouamane Laanait, Joshua Romero, Junqi Yin, M. Todd Young, Sean Treichler, Vitalii Starchenko, Albina Borisevich, Alex Sergeev, Michael Matheson

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Tags: Computer science, Deep learning, Neural networks, nVidia, Package, Tesla V100

September 29, 2019 by hgpu

Elastic deep learning in multi-tenant GPU cluster

Yidi Wu, Kaihao Ma, Xiao Yan, Zhi Liu, James Cheng

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Tags: Computer science, Deep learning, Distributed computing, GPU cluster, nVidia, nVidia GeForce GTX 1080 Ti, Tesla V100

September 29, 2019 by hgpu

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut

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Tags: Artificial intelligence, Computer science, Deep learning, NLP, TPU

September 29, 2019 by hgpu

Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper, Bryan Catanzaro

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Tags: Computer science, Deep learning, NLP, nVidia, nVidia DGX-2, Package, Tesla V100

September 22, 2019 by hgpu

Performance and Power Evaluation of AI Accelerators for Training Deep Learning Models

Yuxin Wang, Qiang Wang, Shaohuai Shi, Xin He, Zhenheng Tang, Kaiyong Zhao, Xiaowen Chu

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Tags: AI, AMD Radeon R7, ATI, Computer science, Deep learning, Neural networks, nVidia, PyTorch, TensorFlow, Tesla P100, Tesla V100, TPU

September 22, 2019 by hgpu

Espresso: A Fast End-to-end Neural Speech Recognition Toolkit

Yiming Wang, Tongfei Chen, Hainan Xu, Shuoyang Ding, Hang Lv, Yiwen Shao, Nanyun Peng, Lei Xie, Shinji Watanabe, Sanjeev Khudanpur

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Tags: Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX 1080 Ti, Package, Speech recognition

September 22, 2019 by hgpu

FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow

Xuezhe Ma, Chunting Zhou, Xian Li, Graham Neubig, Eduard Hovy

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Tags: Computer science, Deep learning, NLP, nVidia, nVidia GeForce GTX Titan X, Package

September 8, 2019 by hgpu

Compositional Deep Learning in Futhark

Duc Minh Tran, Troels Henriksen, Martin Elsman

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Tags: Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX 2080 Ti, Package, TensorFlow

September 1, 2019 by hgpu

Demystifying the MLPerf Benchmark Suite

Snehil Verma, Qinzhe Wu, Bagus Hanindhito, Gunjan Jha, Eugene B. John, Ramesh Radhakrishnan, Lizy K. John

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Tags: Benchmarking, Computer science, CUDA, Deep learning, Machine learning, nVidia, Package, Tesla P100, Tesla V100

September 1, 2019 by hgpu

Automated Architecture Design for Deep Neural Networks

Steven Abreu

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Tags: Computer science, Deep learning, GPU cluster, Machine learning, Neural networks, nVidia, Tesla K80, Thesis

September 1, 2019 by hgpu

Automatic Compiler Based FPGA Accelerator for CNN Training

Shreyas Kolala Venkataramanaiah, Yufei Ma, Shihui Yin, Eriko Nurvithadhi, Aravind Dasu, Yu Cao, Jae-sun Seo

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Tags: Code generation, Compilers, Computer science, Deep learning, FPGA, Neural networks

August 25, 2019 by hgpu

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

high performance computing on graphics processing units: hgpu.org

MIOpen: An Open Source Library For Deep Learning Primitives

Exascale Deep Learning for Scientific Inverse Problems

Elastic deep learning in multi-tenant GPU cluster

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

Performance and Power Evaluation of AI Accelerators for Training Deep Learning Models

Espresso: A Fast End-to-end Neural Speech Recognition Toolkit

FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow

Compositional Deep Learning in Futhark

Demystifying the MLPerf Benchmark Suite

Automated Architecture Design for Deep Neural Networks

Automatic Compiler Based FPGA Accelerator for CNN Training

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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

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