OpenCL - An effective programming model for data parallel computations at the Cell Broadband Engine

Breitbart, Jens; Fohry, Claudia

doi:10.1109/ipdpsw.2010.5470823

Cited by 20 publications

(10 citation statements)

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“…25 Modern computers often include CPUs, highly parallel GPUs and other types of processors, it is essential to take full advantage of these processing platforms. Breitbart and Fohry 26 proposed an effective programming model based on OpenCL for data parallel computations at the cell broadband engine and achieved excellent experimental results. This programming model had the potential be applied to deformation simulation by changing the way of parallel computing on the physical deformation model.…”

Section: Related Workmentioning

confidence: 99%

GPU-assisted energy asynchronous diffusion parallel computing model for soft tissue deformation simulation

Liao

Yuan

et al. 2014

SIMULATION

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Soft tissue deformation simulation is a key technology of virtual surgical simulation. In this work, we present a graphics processing unit (GPU)-assisted energy asynchronous diffusion parallel computing model which is stable and fast in processing complex models, especially concave surface models. We adopt hexahedral voxels to represent the physical model of soft tissue to improve the visual realistic quality and computing efficiency of deformation simulation. We also adopt the concept of free boundary to simulate soft tissue geometric characteristics more precisely during the deformation process and introduce asynchronous diffusion by using the mechanical energy of mass points to achieve realistic soft tissue deformation effects. In order to meet the requirement of real-time surgery simulation, we accelerate the soft tissue deformation by using OpenCL (Open Computing Language) and optimize the parallel computing process in several means. Experimental results have shown that the GPU-assisted energy asynchronous diffusion parallel computing model for soft tissue deformation simulation implements satisfactory effects on deformation in visual realistic and real-time quality.

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Section: Related Workmentioning

confidence: 99%

GPU-assisted energy asynchronous diffusion parallel computing model for soft tissue deformation simulation

Liao

Yuan

et al. 2014

SIMULATION

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“…Research in OpenCL has focused on algorithm porting [5,6] or programming paradigm comparisons [7]. The limited research in kernel code optimization has resulted in the underutilization of these devices.…”

Section: Figmentioning

confidence: 99%

Methods for Optimizing OpenCL Applications on Heterogeneous Multicore Architectures

Chuand¹,

Hsiao²,

Li³

2013

Appl. Math. Inf. Sci.

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Heterogeneous multicore architectures with CPU and add-on GPUs or streaming processors are now widely used in computer systems. These GPUs provide substantially more computation capability and memory bandwidth compared to traditional multi-cores. Also, because they are highly programmable, they provide the computational performance needed for realistic graphics rendering. Applications with general computations can also be leveraged onto these GPUs. This study discusses the architectures of these highly efficient GPUs and applies a unified programming standard called OpenCL to fully utilize their capabilities. Despite their great potential, applications of these GPUs are challenging because of their diverse underlying architectural characteristics. In this study, several optimizing techniques are applied on OpenCL-compatible heterogeneous multicore architectures to achieve thread-level and data-level parallelisms. The architectural implications of these techniques are discussed. Finally, optimization principles for these architectures will be are proposed. The experimental reveal average speedups of 24 and 430 for non-optimized and optimized kernels, respectively.

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“…The organization of them is handled in the OpenMP package itself, using standards of the operation system. With OpenCL, another package exists, which also uses the internal cache of modern computers for fast calculations [16].…”

Section: Introductionmentioning

confidence: 99%

Power Distribution Management System revisited: Single-thread vs. Multithread Performance

Selman

2016

scjournal

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Power Distribution Management System (PDMS) uses very sophisticated algorithms to deliver reliable and efficient functioning of power distribution networks (PDN). PDNs are represented using very large sparse matrices, whose processing is computationally very demanding. Dividing large PDNs into smaller sub-networks results in smaller sparse matrices, and further processing each sub-network in parallel significantly improves the performance of PDMS. Using multithreading to further process each sub-network however degrades PDMS performance. Single-thread processing of sub-network sparse matrices gives much better performance results, mainly due to the structure of these matrices (indefinite and very sparse) and synchronization overhead involved in multi-thread operations. In this paper an overview of PDMS system is presented, and its performance given single-thread and multiple threads is compared. The results have shown that for some applications, single-threaded implementation in multi-process parallel environment gives better performance than multithreaded implementation. Even with increased processing capabilities of multicore CPUs, it is very challenging to improve

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OpenCL - An effective programming model for data parallel computations at the Cell Broadband Engine

Cited by 20 publications

References 1 publication

GPU-assisted energy asynchronous diffusion parallel computing model for soft tissue deformation simulation

GPU-assisted energy asynchronous diffusion parallel computing model for soft tissue deformation simulation

Methods for Optimizing OpenCL Applications on Heterogeneous Multicore Architectures

Power Distribution Management System revisited: Single-thread vs. Multithread Performance

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