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Comparing Programmer Productivity in OpenACC and CUDA: an Empirical Investigation

Xuechao Li, Po-Chou Shih, Jeffrey Overbey, Cheryl Seals, Alvin Lim

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Tags: Computer science, CUDA, nVidia, OpenACC, Package

March 20, 2017 by hgpu

A Study of Complex Deep Learning Networks on High Performance, Neuromorphic, and Quantum Computers

Thomas E. Potok, Catherine Schuman, Steven R. Young, Robert M. Patton, Federico Spedalieri, Jeremy Liu, Ke-Thia Yao, Garrett Rose, Gangotree Chakma

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Tags: CNN, Computer science, Deep learning, Neural networks, Quantum computing

March 20, 2017 by hgpu

Neural Networks for Beginners. A fast implementation in Matlab, Torch, TensorFlow

Francesco Giannini, Vincenzo Laveglia, Alessandro Rossi, Dario Zanca, Andrea Zugarini

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Tags: Computer science, CUDA, Deep learning, Machine learning, Matlab, Neural networks, nVidia, nVidia GeForce GTX 960, Package, TensorFlow, Torch

March 20, 2017 by hgpu

Compiling Parallel Functional Code with Data Parallel Idealised Algol

Robert Atkey, Michel Steuwer, Sam Lindley, Christophe Dubach

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Tags: Algorithms, ATI, ATI Radeon HD 7970, Computer science, nVidia, nVidia GeForce GTX Titan X, OpenCL, performance portability

March 14, 2017 by hgpu

Large-scale image analysis using docker sandboxing

B. Sengupta, E. Vazquez, M. Sasdelli, Y. Qian, M. Peniak, L. Netherton, G. Delfino

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Tags: Computer science, Computer vision, CUDA, Image processing, nVidia, nVidia Quadro M 6000

March 14, 2017 by hgpu

Massive Exploration of Neural Machine Translation Architectures

Denny Britz, Anna Goldie, Thang Luong, Quoc Le

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Tags: Computer science, Deep learning, Neural networks, NLP, nVidia, Package, Python, TensorFlow, Tesla K40

March 14, 2017 by hgpu

Architectural Principles and Experimentation of Distributed High Performance Virtual Clusters

Andrew J. Younge

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Tags: Cloud, Computer science, CUDA, Distributed computing, Molecular dynamics, nVidia, OpenCL, Tesla C2075, Tesla K20, Thesis, Virtualization

March 9, 2017 by hgpu

Achieving high-performance with a sparse direct solver on Intel KNL

Emmanuel Agullo, Alfredo Buttari, Mikko Byckling, Abdou Guermouche, Ian Masliah

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Tags: Computer science, Energy-efficient computing, Intel Xeon Phi, Sparse direct solvers

March 9, 2017 by hgpu

Optimizing Deep CNN-Based Queries over Video Streams at Scale

Daniel Kang, John Emmons, Firas Abuzaid, Peter Bailis, Matei Zaharia

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Tags: Computer science, Computer vision, Databases, Deep learning, Machine learning, Neural networks, nVidia, nVidia DGX-1, Package, TensorFlow, Tesla P100

March 9, 2017 by hgpu

A Machine-Learning Framework for Design for Manufacturability

Aditya Balu, Sambit Ghadai, Gavin Young, Soumik Sarkar, Adarsh Krishnamurthy

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Tags: CNN, Computer science, Deep learning, Keras, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan X, nVidia Quadro K 2200

March 9, 2017 by hgpu

Decoupled Block-Wise ILU(k) Preconditioner on GPU

Bo Yang, Hui Liu, He Zhong, Zhangxin Chen

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Tags: Computer science, CUDA, Factorization, nVidia, Sparse matrix, Tesla K20

March 9, 2017 by hgpu

Multi-kernel Data Partitioning with Channel on OpenCL-based FPGAs

Zeke Wang, Johns Paul, Bingsheng He, Wei Zhang

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Tags: Computer science, Databases, FPGA, OpenCL, Performance

March 5, 2017 by hgpu

CLUEstering: a high-performance density-based clustering library for scientific computing

PyOMP: OpenMP for Python in Numba

PyOMP: Parallel programming for CPUs and GPUs with OpenMP and Python

GPPerf: Energy-Aware GPU Performance Prediction and Optimization Framework

Understanding GEMM Performance and Energy on NVIDIA Ada Lovelace: A Machine Learning-Based Analytical Approach

HiRep

Scaling SU(2) to 1000 GPUs using HiRep

Gaia AVU-GSR

Performance portability via C++ PSTL, SYCL, OpenMP, and HIP: the Gaia AVU-GSR case study

gpuPairHMM: Ultra-fast GPU-based PairHMM for DNA Variant Calling

gpuPairHMM: High-speed Pair-HMM Forward Algorithm for DNA Variant Calling on GPUs

See all packages

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high performance computing on graphics processing units: hgpu.org

Comparing Programmer Productivity in OpenACC and CUDA: an Empirical Investigation

A Study of Complex Deep Learning Networks on High Performance, Neuromorphic, and Quantum Computers

Neural Networks for Beginners. A fast implementation in Matlab, Torch, TensorFlow

Compiling Parallel Functional Code with Data Parallel Idealised Algol

Large-scale image analysis using docker sandboxing

Massive Exploration of Neural Machine Translation Architectures

Achieving high-performance with a sparse direct solver on Intel KNL

Optimizing Deep CNN-Based Queries over Video Streams at Scale

A Machine-Learning Framework for Design for Manufacturability

Decoupled Block-Wise ILU(k) Preconditioner on GPU

Multi-kernel Data Partitioning with Channel on OpenCL-based FPGAs

Recent source codes

tdg-benchs: benchmarks used to test the performance of taskgraph

SW#SYCL

LLOR: Automatic Repair of OpenMP Programs

Solomon: Simple Off-LOading Macros Orchestrating multiple Notations

CLUEstering: a high-performance density-based clustering library for scientific computing

PyOMP: OpenMP for Python in Numba

GPPerf: Energy-Aware GPU Performance Prediction and Optimization Framework

HiRep

Gaia AVU-GSR

gpuPairHMM: Ultra-fast GPU-based PairHMM for DNA Variant Calling

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