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swCUDA: Auto parallel code translation framework from CUDA to ATHREAD for new generation sunway supercomputer

Maoxue Yu, Guanghao Ma, Zhuoya Wang, Shuai Tang, Yuhu Chen, Yucheng Wang, Yuanyuan Liu, Dongning Jia, Zhiqiang Wei

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Tags: Code generation, Computer science, CUDA, nVidia, nVidia GeForce GTX 1050 Ti

January 21, 2024 by hgpu

Novel insights on atomic synchronization for sort-based group-by on GPUs

Bala Gurumurthy, David Broneske, Martin Schäler, Thilo Pionteck

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Tags: Computer science, Databases, Hashing, Heterogeneous systems, nVidia, nVidia A100, nVidia GeForce GTX 1050 Ti, nVidia GeForce RTX 2080 Ti, nVidia V100, OpenCL, Package, Sorting

August 28, 2023 by hgpu

A High-Performance Computing Cluster for Distributed Deep Learning: A Practical Case of Weed Classification Using Convolutional Neural Network Models

Manuel López-Martínez, Germán Díaz-Flórez, Santiago Villagrana-Barraza, Luis O. Solís-Sánchez, Héctor A. Guerrero-Osuna, Genaro M. Soto-Zarazúa, Carlos A. Olvera-Olvera

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Tags: CUDA, Deep learning, Distributed computing, HPC, Image processing, Neural networks, nVidia, nVidia GeForce GTX 1050 Ti

June 4, 2023 by hgpu

Novel Parallel Approaches to Efficiently Solve Spatial Problems on Heterogeneous CPU-GPU Systems

Andrés Jesús Sánchez Fernández

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Tags: Algorithms, Computer science, Databases, Heterogeneous systems, nVidia, nVidia GeForce GT 1030, nVidia GeForce GTX 1050 Ti, nVidia GeForce GTX 980, Thesis

July 3, 2022 by hgpu

Optimization of Heterogeneous Parallel Computing Systems using Machine Learning

Devi Abhiseshu Adurti, Mohit Battu

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Tags: Computer science, CUDA, Heterogeneous systems, Machine learning, Matrix multiplication, nVidia, nVidia GeForce GTX 1050 Ti, Thesis

July 4, 2021 by hgpu

Benchmarking the Nvidia GPU Lineage: From Early K80 to Modern A100 with Asynchronous Memory Transfers

Martin Svedin, Steven W. D. Chien, Gibson Chikafa, Niclas Jansson, Artur Podobas

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Tags: Benchmarking, Computer science, MPI, nVidia, nVidia GeForce GTX 1050 Ti, nVidia Quadro K 2200, Tesla A100, Tesla K80, Tesla P100, Tesla V100

June 20, 2021 by hgpu

An Investigation of Atomic Synchronization for Sort-Based Group-By Aggregation on GPUs

Bala Gurumurthy, David Broneske, Martin Schaler, Thilo Pionteck, Gunter Saake

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Tags: Computer science, Databases, Hashing, Heterogeneous systems, nVidia, nVidia GeForce GTX 1050 Ti, nVidia GeForce RTX 2080 Ti, OpenCL, Sorting

April 5, 2021 by hgpu

BASEMENT v3: a modular freeware for river process modelling over multiple computational backends

Davide Vanzo, Samuel Peter, Lukas Vonwiller, Matthias Buergler, Manuel Weberndorfer, Annunziato Siviglia, Daniel Conde, David F. Vetsch

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Tags: cfd, Computational Physics, CUDA, Fluid dynamics, nVidia, nVidia GeForce GTX 1050 Ti, nVidia GeForce GTX 1080 Ti, Package, Tesla P100

February 28, 2021 by hgpu

Transparent Acceleration of Java-based Deep Learning Engines

Athanasios Stratikopoulos, Mihai-Cristian Olteanu, Ian Vaughan, Zoran Sevarac, Nikos Foutris, Juan Fumero, Christos Kotselidis

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Tags: AMD RX Vega 64, ATI, Computer science, Computer vision, Deep learning, Java, nVidia, nVidia GeForce GTX 1050 Ti, OpenCL, Package

October 4, 2020 by hgpu

OpenCL-Darknet: implementation and optimization of OpenCL-based deep learning object detection framework

Yongbon Koo, Sunghoon Kim, Young-guk Ha

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Tags: AMD Radeon R7, Computer science, CUDA, Deep learning, Heterogeneous systems, Image processing, nVidia, nVidia GeForce GTX 1050 Ti, OpenCL, Package

April 19, 2020 by hgpu

PySPH: a Python-based framework for smoothed particle hydrodynamics

Prabhu Ramachandran, Kunal Puri, Aditya Bhosale, A Dinesh, Abhinav Muta, Pawan Negi, Rahul Govind, Suraj Sanka, Pankaj Pandey, Chandrashekhar Kaushik, Anshuman Kumar, Ananyo Sen, Rohan Kaushik, Mrinalgouda Patil, Deep Tavker, Dileep Menon, Vikas Kurapati, Amal S Sebastian, Arkopal Dutt, Arpit Agarwal

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Tags: cfd, Computational Physics, CUDA, Fluid dynamics, Mathematical Software, nVidia, nVidia GeForce GTX 1050 Ti, OpenCL, Package

September 15, 2019 by hgpu

Simple Iterative Incompressible Smoothed Particle Hydrodynamics

Abhinav Muta, Prabhu Ramachandran, Pawan Negi

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Tags: Benchmarking, Computational Physics, Fluid dynamics, Laplace and Poisson equation, nVidia, nVidia GeForce GTX 1050 Ti, nVidia GeForce GTX 1070 Ti, OpenCL, Package, Poisson equation

August 11, 2019 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

swCUDA: Auto parallel code translation framework from CUDA to ATHREAD for new generation sunway supercomputer

Novel insights on atomic synchronization for sort-based group-by on GPUs

A High-Performance Computing Cluster for Distributed Deep Learning: A Practical Case of Weed Classification Using Convolutional Neural Network Models

Novel Parallel Approaches to Efficiently Solve Spatial Problems on Heterogeneous CPU-GPU Systems

Optimization of Heterogeneous Parallel Computing Systems using Machine Learning

Benchmarking the Nvidia GPU Lineage: From Early K80 to Modern A100 with Asynchronous Memory Transfers

An Investigation of Atomic Synchronization for Sort-Based Group-By Aggregation on GPUs

BASEMENT v3: a modular freeware for river process modelling over multiple computational backends

Transparent Acceleration of Java-based Deep Learning Engines

OpenCL-Darknet: implementation and optimization of OpenCL-based deep learning object detection framework

PySPH: a Python-based framework for smoothed particle hydrodynamics

Simple Iterative Incompressible Smoothed Particle Hydrodynamics

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