high performance computing on graphics processing units: hgpu.org

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TensorNetwork for Machine Learning

Stavros Efthymiou, Jack Hidary, Stefan Leichenauer

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

June 20, 2019 by hgpu

High-Performance Deep Learning via a Single Building Block

Evangelos Georganas, Kunal Banerjee, Dhiraj Kalamkar, Sasikanth Avancha, Anand Venkat, Michael Anderson, Greg Henry, Hans Pabst, Alexander Heinecke

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Tags: Computer science, Deep learning, LSTM, Machine learning, Neural networks, OpenCL, Performance

June 20, 2019 by hgpu

Tensor Processing Units for Financial Monte Carlo

Francois Belletti, Davis King, Kun Yang, Roland Nelet, Yusef Shafi, Yi-Fan Chen, John Anderson

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Tags: Cloud, Deep learning, Finance, Mixed precision, Neural networks, nVidia, Option pricing, Tesla V100, TPU

June 12, 2019 by hgpu

Performance Modelling of Deep Learning on Intel Many Integrated Core Architectures

Andre Viebke, Sabri Pllana, Suejb Memeti, Joanna Kolodziej

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

June 12, 2019 by hgpu

Parallel scalable simulations of biological neural networks using TensorFlow: A beginner’s guide

Saptarshi Soham Mohanta, Collins Assisi

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Tags: Biology, Computer science, CUDA, Deep learning, Differential equations, Machine learning, Neural networks, nVidia, ODEs, Ordinary differential equations, Package, Partial differential equations, PDEs, Python, TensorFlow, Tutorial

June 12, 2019 by hgpu

Dynamic Distribution Pruning for Efficient Network Architecture Search

Xiawu Zheng, Rongrong Ji, Lang Tang, Yan Wan, Baochang Zhang, Yongjian Wu, Yunsheng Wu, Ling Shao

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

June 5, 2019 by hgpu

Classify QCD phase transition with deep learning

Long-Gang Pang, Kai Zhou, Nan Su, Hannah Petersen, Horst Stoecker, Xin-Nian Wang

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Tags: Deep learning, HEP, Machine learning, Neural networks, OpenCL, Package, Pattern recognition, Phase transition, Physics, QCD

June 2, 2019 by hgpu

Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models

Yunfei Teng, Wenbo Gao, Francois Chalus, Anna Choromanska, Donald Goldfarb, Adrian Weller

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Tags: Algorithms, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 1080

June 2, 2019 by hgpu

The Impact of GPU DVFS on the Energy and Performance of Deep Learning: an Empirical Study

Zhenheng Tang, Yuxin Wang, Qiang Wang, Xiaowen Chu

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Tags: Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX 2080 Ti, Performance, Tesla P100, Tesla V100

May 30, 2019 by hgpu

Acceleration of Scientific Deep Learning Models on Heterogeneous Computing Platform with Intel FPGAs

C. Jiang, D. Ojika, T. Kurth, Prabhat, S. Vallecorsa, B. Patel, H. Lam

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Tags: Computer science, Deep learning, FPGA, Heterogeneous systems, Neural networks

May 26, 2019 by hgpu

Performance Analysis of Deep Learning Workloads on Leading-edge Systems

Yihui Ren, Shinjae Yoo, Adolfy Hoisie

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Tags: Cloud, Computer science, Computer vision, CUDA, Deep learning, Machine learning, NLP, nVidia, nVidia DGX-2, nVidia GeForce RTX 2080 Ti, Performance, Tesla V100

May 23, 2019 by hgpu

A Case Study: Exploiting Neural Machine Translation to Translate CUDA to OpenCL

Yonghae Kim, Hyesoon Kim

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Tags: Code generation, Computer science, CUDA, Deep learning, LSTM, NLP, nVidia, OpenCL

May 23, 2019 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

TensorNetwork for Machine Learning

High-Performance Deep Learning via a Single Building Block

Performance Modelling of Deep Learning on Intel Many Integrated Core Architectures

Parallel scalable simulations of biological neural networks using TensorFlow: A beginner’s guide

Dynamic Distribution Pruning for Efficient Network Architecture Search

Classify QCD phase transition with deep learning

Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models

The Impact of GPU DVFS on the Energy and Performance of Deep Learning: an Empirical Study

Acceleration of Scientific Deep Learning Models on Heterogeneous Computing Platform with Intel FPGAs

A Case Study: Exploiting Neural Machine Translation to Translate CUDA to OpenCL

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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