high performance computing on graphics processing units: hgpu.org

hgpu.org » Tesla K80

Distributed Training Large-Scale Deep Architectures

Shang-Xuan Zou, Chun-Yen Chen, Jui-Lin Wu, Chun-Nan Chou, Chia-Chin Tsao, Kuan-Chieh Tung, Ting-Wei Lin, Cheng-Lung Sung, Edward Y. Chang

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Tags: Algorithms, Benchmarking, Computer science, CUDA, Data parallelism, Deep learning, nVidia, Tesla K80

September 21, 2017 by hgpu

Scaling Deep Learning on GPU and Knights Landing clusters

Yang You, Aydin Buluc, James Demmel

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Tags: Cloud, Computer science, Deep learning, GPU cluster, Intel Xeon Phi, Machine learning, Neural networks, nVidia, Package, PC cluster, Tesla K80, Tesla M100

August 17, 2017 by hgpu

Bifrost: a Python/C++ Framework for High-Throughput Stream Processing in Astronomy

Miles D. Cranmer, Benjamin R. Barsdell, Danny C. Price, Jayce Dowell, Hugh Garsden, Veronica Dike, Tarraneh Eftekhari, Alexander M. Hegedus, Joseph Malins, Kenneth S. Obenberger, Frank Schinzel, Kevin Stovall, Gregory B. Taylor, Lincoln J. Greenhill

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Tags: Astrophysics, Computer science, CUDA, nVidia, Package, Python, Tesla K80

August 8, 2017 by hgpu

Scalability Study of Deep Learning Algorithms in High Performance Computer Infrastructures

Francesc Sastre Cabot

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Tags: Algorithms, Computer science, Computer vision, CUDA, Deep learning, GPU cluster, Machine learning, Neural networks, nVidia, Tesla K80, Tesla M2090, Thesis

July 22, 2017 by hgpu

Parallelization and Performance of the NIM Weather Model on CPU, GPU and MIC Processors

Mark Govett, Jim Rosinski, Jacques Middlecoff, Tom Henderson, Jin Lee, Alexander MacDonald, Ning Wang, Paul Madden, Julie Schramm, Antonio Duarte

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Tags: Earth and Space Sciences, Intel Xeon Phi, nVidia, OpenACC, Tesla K80, Weather prediction, Weather Prediction Model

July 19, 2017 by hgpu

A Similarity Measure for GPU Kernel Subgraph Matching

Robert V. Lim, Boyana Norris, Allen D. Malony

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Tags: Computer science, CUDA, nVidia, Performance, Tesla K80, Tesla M40, Tesla P100

July 14, 2017 by hgpu

A Linear Algebra Approach to Fast DNA Mixture Analysis Using GPUs

Siddharth Samsi, Brian Helfer, Jeremy Kepner, Albert Reuther, Darrell O. Ricke

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Tags: Algorithms, Computer science, CUDA, Genomics, Linear Algebra, Matrix multiplication, nVidia, Performance, Tesla K80

July 5, 2017 by hgpu

Multi-level Parallelism with MPI and OpenACC for CFD Applications

Andrew J. McCall

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Tags: cfd, Fluid dynamics, Fortran, MPI, nVidia, OpenACC, Tesla C2075, Tesla K80, Tesla M2050, Thesis

June 21, 2017 by hgpu

Kapre: On-GPU Audio Preprocessing Layers for a Quick Implementation of Deep Neural Network Models with Keras

Keunwoo Choi, Deokjin Joo, Juho Kim

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Tags: Computer science, CUDA, Deep learning, Keras, Neural networks, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX Titan X, Package, Python, Signal processing, Tesla K80, Tesla M60

June 21, 2017 by hgpu

Device Placement Optimization with Reinforcement Learning

Azalia Mirhoseini, Hieu Pham, Quoc V. Le, Benoit Steiner, Rasmus Larsen, Yuefeng Zhou, Naveen Kumar, Mohammad Norouzi, Samy Bengio, Jeff Dean

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Tags: Artificial intelligence, Computer science, CUDA, Deep learning, Heterogeneous systems, Neural networks, nVidia, TensorFlow, Tesla K80

June 17, 2017 by hgpu

Numerical Model of Shallow Water: the Use of NVIDIA CUDA Graphics Processors

Tatyana Dyakonova, Alexander Khoperskov, Sergey Khrapov

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Tags: cfd, Computer science, CUDA, Fluid dynamics, Numerical simulation, nVidia, Tesla C2070, Tesla K20, Tesla K40, Tesla K80

May 6, 2017 by hgpu

Evaluation of GPU-based track-triggering for the CMS detector at CERN’s HL-LHC

Hannes Mohr

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Tags: Algorithms, ASIC, CUDA, FPGA, HEP, nVidia, Physics, Tesla K40, Tesla K80, Thesis

April 20, 2017 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

Distributed Training Large-Scale Deep Architectures

Bifrost: a Python/C++ Framework for High-Throughput Stream Processing in Astronomy

Scalability Study of Deep Learning Algorithms in High Performance Computer Infrastructures

Parallelization and Performance of the NIM Weather Model on CPU, GPU and MIC Processors

A Similarity Measure for GPU Kernel Subgraph Matching

A Linear Algebra Approach to Fast DNA Mixture Analysis Using GPUs

Multi-level Parallelism with MPI and OpenACC for CFD Applications

Kapre: On-GPU Audio Preprocessing Layers for a Quick Implementation of Deep Neural Network Models with Keras

Device Placement Optimization with Reinforcement Learning

Numerical Model of Shallow Water: the Use of NVIDIA CUDA Graphics Processors

Evaluation of GPU-based track-triggering for the CMS detector at CERN’s HL-LHC

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