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Profiling based Out-of-core Hybrid Method for Large Neural Networks

Yuki Ito, Haruki Imai, Tung Le Duc, Yasushi Negishi, Kiyokuni Kawachiya, Ryo Matsumiya, Toshio Endo

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Tags: Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, Performance, Tesla V100

July 14, 2019 by hgpu

PANNA: Properties from Artificial Neural Network Architectures

Ruggero Lot, Franco Pellegrini, Yusuf Shaidu, Emine Kucukbenli

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Tags: Deep learning, Machine learning, Molecular dynamics, Neural networks, Package, Physics, TensorFlow

July 10, 2019 by hgpu

A Unified Optimization Approach for CNN Model Inference on Integrated GPUs

Leyuan Wang, Zhi Chen, Yizhi Liu, Yao Wang, Lianmin Zheng, Mu Li, Yida Wang

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Tags: ARM, CNN, Compilers, Computer science, Deep learning, Machine learning, Neural networks, nVidia, nVidia Jetson Nano, OpenCL, Package

July 7, 2019 by hgpu

Themis: Fair and Efficient GPU Cluster Scheduling for Machine Learning Workloads

Kshiteej Mahajan, Arjun Singhvi, Arjun Balasubramanian, Varun Batra, Surya Teja Chavali, Shivaram Venkataraman, Aditya Akella, Amar Phanishayee, Shuchi Chawla

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Tags: Computer science, GPU cluster, Machine learning, nVidia, Task scheduling, Tesla K80, Tesla M60

July 4, 2019 by hgpu

A Static Analysis-based Cross-Architecture Performance Prediction Using Machine Learning

Newsha Ardalani, Urmish Thakker, Aws Albarghouthi, Karu Sankaralingam

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Tags: Computer science, Machine learning, nVidia, nVidia GeForce GTX 660 Ti, nVidia GeForce GTX 750, Performance

June 23, 2019 by hgpu

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

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

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

CU2CL: A prototype CUDA-to-OpenCL source-to-source translator

On the Portability of CPU-Accelerated Applications via Automated Source-to-Source Translation

GPU-based Parallel Computation Support for Stan

PANNA: Properties from Artificial Neural Network Architectures

Artifact Evaluation for Novel methodologies for predictable CPU-to-GPU command offloading

Novel Methodologies for Predictable CPU-To-GPU Command Offloading

tvm: Open deep learning compiler stack for cpu, gpu and specialized accelerators

A Unified Optimization Approach for CNN Model Inference on Integrated GPUs

picongpu: Particle-in-Cell Simulations for the Exascale Era

PIConGPU: Predictive Simulations of Laser-Particle Accelerators with Manycore Hardware

WCCV: Warp-coherent Condition Vectorization(on top of RV)

WCCV: Improving the Vectorization of IF-statements with Warp-Coherent Conditions

Triton: An Intermediate Language and Compiler for Tiled Neural Network Computations

HEATS: Heterogeneity- and Energy-Aware Task-based Scheduling

HAM: Heterogeneous Active Messages C++ library

Heterogeneous Active Messages (HAM) – Implementing Lightweight Remote Procedure Calls in C++

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Boston, USA

7th International Workshop on OpenCL, 2019

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Boston, MA, USA

Distributed Heterogeneous Programming in C/C++ (DHPCC++), 2019

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Larnaca, Cyprus

3rd International Workshop on Theoretical Approaches to Performance Evaluation, Modeling and Simulation (TAPEMS), 2019

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26-28

Rome, Italy

International Conference on Frontiers of Neural Networks (ICFNN), 2019

2019

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26-28

Rome, Italy

International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML), 2019

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high performance computing on graphics processing units: hgpu.org

Profiling based Out-of-core Hybrid Method for Large Neural Networks

PANNA: Properties from Artificial Neural Network Architectures

A Unified Optimization Approach for CNN Model Inference on Integrated GPUs

Themis: Fair and Efficient GPU Cluster Scheduling for Machine Learning Workloads

A Static Analysis-based Cross-Architecture Performance Prediction Using Machine Learning

TensorNetwork for Machine Learning

High-Performance Deep Learning via a Single Building Block

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

Recent source codes

CU2CL: A prototype CUDA-to-OpenCL source-to-source translator

GPU-based Parallel Computation Support for Stan

PANNA: Properties from Artificial Neural Network Architectures

Artifact Evaluation for Novel methodologies for predictable CPU-to-GPU command offloading

tvm: Open deep learning compiler stack for cpu, gpu and specialized accelerators

picongpu: Particle-in-Cell Simulations for the Exascale Era

WCCV: Warp-coherent Condition Vectorization(on top of RV)

Triton: An Intermediate Language and Compiler for Tiled Neural Network Computations

HEATS: Heterogeneity- and Energy-Aware Task-based Scheduling

HAM: Heterogeneous Active Messages C++ library

Most viewed papers (last 30 days)

Featured events

7th International Workshop on OpenCL, 2019

Distributed Heterogeneous Programming in C/C++ (DHPCC++), 2019

3rd International Workshop on Theoretical Approaches to Performance Evaluation, Modeling and Simulation (TAPEMS), 2019

International Conference on Frontiers of Neural Networks (ICFNN), 2019

International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML), 2019