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中文標題: 非線性動力結構分析之 GPGPU 平行化與效能評估
英文標題: GPGPU Parallelism and Performance Evaluation of Nonlinear Dynamic Structural Analysis
編號: NCREE-11-023
語言: 中文
編輯:
媒體型式: 紙本
作者:
楊元森   許國志   
中文摘要: 大型非線性結構在進行分析時,常因為運算量太過龐大,而導致電腦
運算時間過長。因此在分析上,折衷採用分析運算量較少但精度較低的數
值模型。為了要解決運算量的問題,各式各樣的高速運算方法不斷的運用
在科學運算中。GPGPU 的超多核心與低成本的特性,使得GPGPU 有潛力
成為進行大型非線性結構分析的另一個工具。
隨著科技發展越來越進步,繪圖處理器 (Graphics Processing Unit,簡
稱GPU)具有「超多」核心(many-core)的特性,其運算能力隨之變得非常強
大。近年來,GPU 的超多核心的特性更進一步地被發展為適用於一般運算
用途的處理器,而此技術被稱為GPGPU (General-Purpose computing on
Graphics Processing Unit)。本研究重新檢視OpenSees (Open System for
Earthquake Engineering Simulation)程式運算流程,並建立一個新的平行運算
流程,以適用於GPGPU 的超多核心特性。本研究將新的平行運算流程實作
於美國加州大學柏克萊分校的OpenSees 地震工程分析系統。測試結果顯
示,本研究所測試的平行運算流程,配合GPGPU 技術,縮短大型非線性動
力分析約20%的時間。
英文摘要: With the advances of technology, graphics processor (Graphics Processing Unit, called GPU) with “many”-core technique subsequently became very powerful computing power. In recent years, the GPU technology was further developed for general purpose computing. This technology is known as GPGPU (General-Purpose computing on Graphics Processing Unit). The GPGPU technology brings an opportunity for large-scale nonlinear dynamic structural analyses with refined numerical models. These analyses are not commonly carried out, even if their results are more reliable and accurate. One of the reasons is their long computing time. At present, engineers and researchers commonly adopt simple and coarse numerical models to obtain analysis results quickly. Based GPGPU’s “many”-core feature, this work re-examined the procedure of finite element structural analysis, and proposed a new procedure for GPGPU computing. The new GPGPU procedure was implemented in an open-source structural analysis system named OpenSees (Open System for Earthquake Engineering Simulation). Numerical experiments were conducted to verify the performance of GPGPU OpenSees. The experimental data showed that the GPGPU enhanced OpenSees requires up to 80% of time cost of the original version in our test. The proposed procedure has potential to reduce the execution time of large-scale nonlinear dynamic structural analyses.
關鍵字: GPGPU、OpenSees、OpenCL
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