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About 3,190 papers for “OpenCL”

OpenCL

Kovács, György · University of Debrecen Electronic Archive (University of Debrecen) · 2016

Cited by 0

OpenCL

Kovács, György · University of Debrecen Electronic Archive (University of Debrecen) · 2016

Cited by 0

OpenCL

Matthias Noack, Florian Wende, Klaus-Dieter Oertel · Elsevier eBooks · 2015

Cited by 5

Hybrid OpenCL: Enhancing OpenCL for Distributed Processing

Ryo Aoki, Shuichi Oikawa, Takashi Nakamura et al. · 2011

We have been developing Hybrid OpenCL, which enables the utilization of OpenCL devices by connecting them over the network. Hybrid OpenCL opens a gate to scale up OpenCL environments. By using Hybrid OpenCL, applications written in OpenCL can be easily ported to high performance cluster computers, thus, Hybrid OpenCL can provide more various distributed and parallel computing platforms. Hybrid OpenCL consists of two elements, a runtime system that provides the abstraction of OpenCL implementations and a bridge program that connects multiple OpenCL runtime systems over the network. This paper describes the design and implementation Hybrid OpenCL. Its performance for distributed processing is evaluated by using multiple nodes. The experimentation results show that the overhead introduced by Hybrid OpenCL is minimum and comparable with the combination of MPI and OpenCL.

Cited by 29

FWHT-OpenCL

Pereira, Pedro M. M. · Zenodo (CERN European Organization for Nuclear Research) · 2017

The Walsh-Hadamard transform plays a major role in many image and video coding algorithms. In one hand, its intensive use in these algorithms makes its acceleration a challenge, in order to speed-up the algorithm execution. On the other hand, the available fast implementations are not efficient across different platforms. In this work, a parallel-based implementation of the WHT is proposed for CPU and GPU platforms using the OpenCL standard. OpenCL achieves portability at code level, but its performance suffers when the same code is used for CPUs and GPUs. To achieve top performance, we propose two WHT versions: OpenCL-GPU for GPUs and OpenCL-CPU for CPUs.

Cited by 0Open access

OpenCL standard

Jakub Michlovský · Digital Repository (National Repository of Grey Literature) · 2011

This work discussed about the OpenCL standard, which can use video card for accela- ration intensice calculations. In first part is an detail analyze of architecture of standard and support by major hardware manufacturers and operating systems. In the second part is described a creation and implementation of the application for adjustment of the picture. Whole work is ended by comparative measurment of the time of recon to processor and on the graphic card.

Cited by 0

OpenCL standard

Jakub Michlovský · Digital Repository (National Repository of Grey Literature) · 2011

This work discussed about the OpenCL standard, which can use video card for accela- ration intensice calculations. In first part is an detail analyze of architecture of standard and support by major hardware manufacturers and operating systems. In the second part is described a creation and implementation of the application for adjustment of the picture. Whole work is ended by comparative measurment of the time of recon to processor and on the graphic card.

Cited by 0

Standard OpenCL

Michlovský, Jakub · Brno University of Technology Digital Library (Brno University of Technology) · 2011

Tato práce pojednavá o standardu OpenCL, který umí použít grafickou kartu pro akce- laraci náročných výpočtů. V první části práce je podrobně rozebrána architektura stan- dardu a podporova hlavních výrobců hardware a operačních systémů. Ve druhé části je rozepsána tvorba a implementace ukázkové aplikace, která provádí úpravy obrázku. Celá práce je zakončena srovnávacím měřením doby výpočtů na procesoru a na grafické kartě.

Cited by 0Open access

OpenCL Lecture

Carr, Leslie · EdShare Southampton (University of Southampton) · 2009

Cited by 0

OpenCL standard

Michlovský, Jakub · Brno University of Technology Digital Library (Brno University of Technology) · 2011

Tato práce pojednavá o standardu OpenCL, který umí použít grafickou kartu pro akce- laraci náročných výpočtů. V první části práce je podrobně rozebrána architektura stan- dardu a podporova hlavních výrobců hardware a operačních systémů. Ve druhé části je rozepsána tvorba a implementace ukázkové aplikace, která provádí úpravy obrázku. Celá práce je zakončena srovnávacím měřením doby výpočtů na procesoru a na grafické kartě.

Cited by 0

OpenCL Extensions

Benedict R. Gaster, Lee Howes, David Kaeli et al. · Elsevier eBooks · 2013

Cited by 0

OpenCL vs

Harri Renney, Benedict R. Gaster, T. M. Mitchell · 2019

Digital audio synthesis has become an important component of modern music production with techniques that can produce realistic simulations of real instruments. Physical modelling sound synthesis is a category of audio synthesis that uses mathematical models to emulate the physical phenomena of acoustic musical instruments including drum membranes, air columns and strings. The synthesis of physical phenomena can be expressed as discrete variants of Newton's laws of motion, using, for example, the Finite-Difference Time-Domain method or FDTD.

Cited by 2

OpenCL Extensions

Benedict R. Gaster, Lee Howes, David Kaeli et al. · Elsevier eBooks · 2011

Cited by 0

Using OpenCL

Kowalik Janusz, Pu zacute niakowski Tadeusz · Advances in parallel computing · 2012

Cited by 1

Distributed OpenCL Distributing OpenCL Platform on Network Scale

Barış Eskikaya, D. Turgay Altılar · Istanbul Technical University Academic Open Archive (Istanbul Technical University) · 2012

This paper presents a framework that extends OpenCL by distributing computing process to many computing resources connected via network and enables the computing resources to run in parallel. Using JSON RPC (Remote Procedure Call technique relying on JavaScript Object Notation) in communication layer, Distributed OpenCL framework provides platform and operating system independency. Using this framework, a host program executed on a computer that has no OpenCL support is able to use other computing resources distributed on network in parallel. Results show that OpenCL platform model can be extended to network scale to provide a vendor, architecture and operating system independent and a parallel computing environment with reasonable communication overhead.

Cited by 9Open access

OpenCL execution model.

Yupu Song (12501028), Cailin Li (17743113), Shiyang Xiao (17743116) et al. · Figshare · 2024

<div><p>In the process of Canny edge detection, a large number of high complexity calculations such as Gaussian filtering, gradient calculation, non-maximum suppression, and double threshold judgment need to be performed on the image, which takes up a lot of operation time, which is a great challenge to the real-time requirements of the algorithm. The traditional Canny edge detection technology mainly uses customized equipment such as DSP and FPGA, but it has some problems, such as long development cycle, difficult debugging, resource consumption, and so on. At the same time, the adopted CUDA platform has the problem of poor cross-platform. In order to solve this problem, a fine-grained parallel Canny edge detection method is proposed, which is optimized from three aspects: task partition, vector memory access, and NDRange optimization, and CPU-GPU collaborative parallelism is realized. At the same time, the parallel Canny edge detection methods based on multi-core CPU and CUDA architecture are designed. The experimental results show that OpenCL accelerated Canny edge detection algorithm (OCL_Canny) achieves 20.68 times acceleration ratio compared with CPU serial algorithm at 7452 × 8024 image resolution. At the image resolution of 3500 × 3500, the OCL_Canny algorithm achieves 3.96 times the acceleration ratio compared with the CPU multi-threaded Canny parallel algorithm. At 1024 × 1024 image resolution, the OCL_Canny algorithm achieves 1.21 times the acceleration ratio compared with the CUDA-based Canny parallel algorithm. The effectiveness and performance portability of the proposed Canny edge detection parallel algorithm are verified, and it provides a reference for the research of fast calculation of image big data.</p></div>

Cited by 0Open access

OpenCL platform model.

Yupu Song (12501028), Cailin Li (17743113), Shiyang Xiao (17743116) et al. · Figshare · 2024

<div><p>In the process of Canny edge detection, a large number of high complexity calculations such as Gaussian filtering, gradient calculation, non-maximum suppression, and double threshold judgment need to be performed on the image, which takes up a lot of operation time, which is a great challenge to the real-time requirements of the algorithm. The traditional Canny edge detection technology mainly uses customized equipment such as DSP and FPGA, but it has some problems, such as long development cycle, difficult debugging, resource consumption, and so on. At the same time, the adopted CUDA platform has the problem of poor cross-platform. In order to solve this problem, a fine-grained parallel Canny edge detection method is proposed, which is optimized from three aspects: task partition, vector memory access, and NDRange optimization, and CPU-GPU collaborative parallelism is realized. At the same time, the parallel Canny edge detection methods based on multi-core CPU and CUDA architecture are designed. The experimental results show that OpenCL accelerated Canny edge detection algorithm (OCL_Canny) achieves 20.68 times acceleration ratio compared with CPU serial algorithm at 7452 × 8024 image resolution. At the image resolution of 3500 × 3500, the OCL_Canny algorithm achieves 3.96 times the acceleration ratio compared with the CPU multi-threaded Canny parallel algorithm. At 1024 × 1024 image resolution, the OCL_Canny algorithm achieves 1.21 times the acceleration ratio compared with the CUDA-based Canny parallel algorithm. The effectiveness and performance portability of the proposed Canny edge detection parallel algorithm are verified, and it provides a reference for the research of fast calculation of image big data.</p></div>

Cited by 0Open access