研究生: |
羅仕為 Lo, Shih-Wei |
---|---|
論文名稱: |
基於OpenACC之GPU加速性能案例研究 Performance case studies for OpenACC-based GPU acceleration |
指導教授: |
陳人豪
Chen, Jen-Hao |
口試委員: |
劉晉良
Liu, Jinn-Liang 陳仁純 Chen, Ren-Chuen |
學位類別: |
碩士 Master |
系所名稱: |
理學院 - 計算與建模科學研究所 Institute of Computational and Modeling Science |
論文出版年: | 2019 |
畢業學年度: | 107 |
語文別: | 英文 |
論文頁數: | 28 |
中文關鍵詞: | 平行化 、顯示卡計算 、OpenACC 、BiCGSTAB演算法 、與時間相關之薛丁格方程式 |
外文關鍵詞: | parallelization, GPU, OpenACC, BiCGSTAB, TDSE |
相關次數: | 點閱:1 下載:0 |
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在本論文中,呈現了以OpenACC來使用GPU計算的加速性能。一般常見使用GPU計算的工具是使用CUDA,我們使用OpenACC的原因是使用它比CUDA更為簡單。我們將其運用於兩個不同的案例,第一個是將BiCGSTAB平行化,用來解Poisson-Fermi模型構建的TRPV通道矩陣,另一個則是以split-operation方法來解與時間相關的薛丁格方程式。結果表明,以OpenACC使用GPU加速,可以將這兩個案例分別加速大約45.1倍以及52.3倍。
This thesis presents graphics processing unit (GPU) parallelization by using OpenACC. The reason for choosing OpenACC is that the usage of OpenACC is more simpler than compute unified device architecture (CUDA). Two cases are performed to test the performance of OpenACC. One is the Poisson-Fermi model for TRPV channel simulation solved by biconjugate gradient stabilized method (BiCGSTAB), and another case is that solve time-dependent Schrödinger equation (TDSE) by using split-operation method. The results show that OpenACC can achieve 45.1X and 52.3X speedups for the cases of TRPV and TDSE, respectively.
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