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Performance Analysis of Open Source Machine Learning Frameworks for Various Parameters in Single-Threaded and Multi-Threaded Modes

Yuriy Kochura, Sergii Stirenko, Oleg Alienin, Michail Novotarskiy, Yuri Gordienko

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Tags: Computer science, CUDA, Deep learning, Image recognition, Java, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan, Performance, TensorFlow, Tesla K40

September 3, 2017 by hgpu

GARDENIA: A Domain-specific Benchmark Suite for Next-generation Accelerators

Zhen Xu, Xuhao Chen, Jie Shen, Yang Zhang, Cheng Chen, Canqun Yang

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Tags: Benchmarking, Computer science, CUDA, Graph theory, Machine learning, nVidia, Package, Tesla K40

August 17, 2017 by hgpu

Directive-Based Partitioning and Pipelining for Graphical Processing Units

X. Cui, T. R. W. Scogland, B. R. de Supinski, W. Feng

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Tags: ATI, ATI Radeon HD 7970, Computer science, CUDA, nVidia, OpenACC, OpenCL, OpenMP, Performance, Task scheduling, Tesla K40

August 1, 2017 by hgpu

FUX-Sim: Implementation of a fast universal simulation/reconstruction framework for X-ray systems

Monica Abella, Estefania Serrano, Javier Garcia-Blas, Ines Garcia, Claudia de Molina, Jesus Carretero, Manuel Desco

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Tags: Algorithms, Computer science, CUDA, Image reconstruction, nVidia, OpenCL, Tesla K40

July 25, 2017 by hgpu

Automatically Selecting Profitable Thread Block Sizes Using Machine Learning

Tiffany A. Connors

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Tags: Computer science, CUDA, Machine learning, nVidia, Performance, Tesla K40, Thesis

July 22, 2017 by hgpu

GPU LSM: A Dynamic Dictionary Data Structure for the GPU

Saman Ashkiani, Shengren Li, Martin Farach-Colton, Nina Amenta, John D. Owens

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Tags: Algorithms, Computer science, CUDA, Databases, nVidia, Sorting, Tesla K40

July 19, 2017 by hgpu

A Fast Method For Computing Principal Curvatures From Range Images

Andrew Spek, Wai Ho Li, Tom Drummond

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Tags: Computer science, Computer vision, CUDA, nVidia, Package, Tesla K40

July 5, 2017 by hgpu

Deep neural networks for direct, featureless learning through observation: the case of 2d spin models

K. Mills, I. Tamblyn

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Tags: Condensed matter, CUDA, Deep learning, Ising model, Materials Science, Neural networks, nVidia, Phase transition, Physics, Tesla K40

July 2, 2017 by hgpu

Scalar collapse in AdS with an OpenCL open source code

Steven L. Liebling, Gaurav Khanna

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Tags: AMD Radeon R9 295X2, ATI, General Relativity and Quantum Cosmology, Heterogeneous systems, High Energy Physics - Theory, nVidia, OpenCL, Package, Physics, Tesla K40

June 25, 2017 by hgpu

Optimizing Memory Efficiency for Convolution Kernels on Kepler GPUs

Xiaoming Chen, Jianxu Chen, Danny Z. Chen, Xiaobo Sharon Hu

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Tags: CNN, Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, Tesla K40

June 1, 2017 by hgpu

Accelerating solutions of PDEs with GPU-based swept time-space decomposition

Daniel J Magee, Kyle E Niemeyer

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Tags: Algorithms, cfd, Computational Physics, CUDA, Differential equations, Fluid dynamics, nVidia, Package, Partial differential equations, PDEs, Physics, Tesla K40

May 11, 2017 by hgpu

Acceleration of Deep Learning on FPGA

Huyuan Li

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Tags: CNN, Computer science, Deep learning, FPGA, Neural networks, nVidia, OpenCL, Tesla K40, Thesis

May 9, 2017 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

Performance Analysis of Open Source Machine Learning Frameworks for Various Parameters in Single-Threaded and Multi-Threaded Modes

GARDENIA: A Domain-specific Benchmark Suite for Next-generation Accelerators

Directive-Based Partitioning and Pipelining for Graphical Processing Units

FUX-Sim: Implementation of a fast universal simulation/reconstruction framework for X-ray systems

Automatically Selecting Profitable Thread Block Sizes Using Machine Learning

GPU LSM: A Dynamic Dictionary Data Structure for the GPU

A Fast Method For Computing Principal Curvatures From Range Images

Deep neural networks for direct, featureless learning through observation: the case of 2d spin models

Scalar collapse in AdS with an OpenCL open source code

Optimizing Memory Efficiency for Convolution Kernels on Kepler GPUs

Accelerating solutions of PDEs with GPU-based swept time-space decomposition

Acceleration of Deep Learning on FPGA

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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