LBM的GPU算法及其在顱內(nèi)動脈瘤血流動力學(xué)中的應(yīng)用
發(fā)布時間：2018-08-27 10:17
【摘要】：顱內(nèi)動脈瘤是一種常見的顱內(nèi)兇險疾病,其破裂后產(chǎn)生的蛛網(wǎng)膜下腔出血具有極高的致殘率和致死率。支架置入術(shù)作為一種治療顱內(nèi)動脈瘤的最新方法受到廣泛關(guān)注,該方法具有微創(chuàng)性、恢復(fù)快、療效好等優(yōu)點(diǎn)。但是,支架的治療效果受到動脈瘤形狀、支架結(jié)構(gòu)等多種因素的影響,需要針對個體真實(shí)動脈瘤模型進(jìn)行深入研究。對于此類問題,現(xiàn)有的臨床觀測、動物實(shí)驗(yàn)和體外實(shí)驗(yàn)等研究手段受到了很大的限制。另一方面,血流動力學(xué)被普遍認(rèn)為是影響動脈瘤發(fā)生、生長、破裂及治療的主要因素之一,因而利用數(shù)值模擬研究動脈瘤內(nèi)血流動力學(xué)參數(shù)的變化是探討支架作用的極佳選擇。傳統(tǒng)數(shù)值方法在研究顱內(nèi)動脈瘤血流動力學(xué)方面會受到血液流體特性、血管復(fù)雜幾何結(jié)構(gòu)以及大規(guī)模計算量等諸多瓶頸的限制,因此需要發(fā)展高效的計算方法和先進(jìn)的并行計算技術(shù)。 20世紀(jì)80年代后期發(fā)展起來的介觀格子Boltzmann方法(LBM)兼有微觀與宏觀模型的優(yōu)點(diǎn),在模擬流體的相互作用和處理復(fù)雜邊界等方面有著傳統(tǒng)方法難以比擬的優(yōu)勢,特別適合用于研究顱內(nèi)動脈瘤內(nèi)的血流流動。此外,LBM具有計算效率高且天然并行等優(yōu)點(diǎn),特別適合利用先進(jìn)的并行計算技術(shù)進(jìn)行加速。與此同時,基于圖形處理器(GPU)的并行技術(shù)近幾年來獲得迅猛的發(fā)展。相比基于傳統(tǒng)CPU的應(yīng)用,基于GPU的應(yīng)用可獲得一到兩個數(shù)量級的加速效果。LBM的天然并行性使得其與GPU具有良好的匹配性,基于GPU的LBM可獲得較理想的加速效果。 但是,目前基于GPU的LBM研究還未夠深入,特別是對于血液流這種復(fù)雜流場內(nèi)流動問題的LBM的實(shí)現(xiàn)與優(yōu)化缺乏系統(tǒng)深入的討論。此外,基于LBM的顱內(nèi)動脈瘤血流動力學(xué)相關(guān)研究中,未實(shí)現(xiàn)對置入支架的個體化真實(shí)動脈瘤的數(shù)值模擬。鑒于此,本文將首先進(jìn)一步發(fā)展基于GPU的LBM,在此基礎(chǔ)上,展開基于醫(yī)學(xué)圖像的個體化真實(shí)顱內(nèi)動脈瘤血流動力學(xué)研究。論文主要工作包括以下兩個部分： (1)在基于GPU的LBM方面,本文首先以方腔流等簡單流場為例,著重探討存儲器訪問優(yōu)化等優(yōu)化技術(shù)的作用,并對程序的性能進(jìn)行了詳細(xì)分析,探討了在GPU處理器上影響LBM程序性能的因素。所設(shè)計的算法性能優(yōu)于國際上相關(guān)算法,與經(jīng)過充分優(yōu)化的CPU程序相比,在Tesla C1060上最高可達(dá)50倍加速比。在此基礎(chǔ)上,針對不同復(fù)雜流場的特點(diǎn),分別基于完全矩陣方法和稀疏矩陣方法提出了兩種算法,并通過大量的數(shù)值模擬分析各自的適用工況。其中稀疏矩陣算法在Tesla C1060上取得250以上MLUPS(每秒百萬網(wǎng)格更新),優(yōu)于國際上相關(guān)結(jié)果(170MLUPS)。最后,將設(shè)計的算法進(jìn)一步推進(jìn)至多GPU平臺,對數(shù)據(jù)訪存和通信過程等方面進(jìn)行優(yōu)化。在配備4塊Tesla C1060的平臺上,完全矩陣算法可取得接近100%的并行效率,稀疏矩陣算法可取得接近90%并行效率。 (2)在顱內(nèi)動脈瘤血流動力學(xué)方面,針對已往大部分研究將血液簡化假設(shè)為牛頓流體的現(xiàn)狀,本文利用不同的動脈瘤模型、不同孔隙率的支架等工況來對比牛頓模型與非牛頓模型得出的結(jié)果,以此探討這種簡化假設(shè)的合理性,分析非牛頓效應(yīng)對研究結(jié)果的影響。研究結(jié)果表明,窄頸動脈瘤和置入低孔隙率支架的動脈瘤更易受到非牛頓效應(yīng)的影響。在此之后,本文從醫(yī)學(xué)圖像出發(fā),通過動脈瘤模型的三維重建、支架的虛擬配置等處理,實(shí)現(xiàn)個體化真實(shí)動脈瘤的數(shù)值模擬,并對相關(guān)血流動力學(xué)問題展開了研究,發(fā)現(xiàn)支架置入位置對窄頸動脈瘤和置入高孔隙率支架的動脈瘤有較大影響,血流進(jìn)入瘤內(nèi)方向發(fā)生明顯改變；發(fā)現(xiàn)在動脈瘤瘤頸近端對支架加密可達(dá)到較好效果,與整體加密的支架功效相近。 總之,本文推進(jìn)了基于GPU的LBM的相關(guān)研究,對復(fù)雜流場內(nèi)流動問題的模擬進(jìn)行了細(xì)致而深入的優(yōu)化。同時,本文利用LBM對顱內(nèi)動脈瘤血流動力學(xué)相關(guān)問題展開了研究,實(shí)現(xiàn)了基于個體化真實(shí)動脈瘤模型的數(shù)值研究。
[Abstract]:Intracranial aneurysms are a common and dangerous intracranial disease. Subarachnoid hemorrhage after rupture of intracranial aneurysms has a high morbidity and mortality rate. Stent implantation as a new treatment of intracranial aneurysms has attracted wide attention. This method has the advantages of minimally invasive, rapid recovery and good curative effect. To study the effect of aneurysm shape, stent structure and other factors, it is necessary to study the real aneurysm model in detail. One of the main factors in rupture and treatment is the use of numerical simulation to study the hemodynamic parameters in aneurysms, which is the best choice to explore the role of stents. Therefore, it is necessary to develop efficient computing methods and advanced parallel computing technology.
The mesoscopic lattice Boltzmann method (LBM), developed in the late 1980s, has the advantages of both microscopic and macroscopic models. It has many advantages over traditional methods in simulating fluid interactions and dealing with complex boundaries. It is especially suitable for studying blood flow in intracranial aneurysms. At the same time, parallel technology based on graphics processor (GPU) has developed rapidly in recent years. Compared with traditional CPU-based applications, GPU-based applications can achieve speedup of one to two orders of magnitude. LBM's natural parallelism makes it possible to speed up with GPU. With good matching, LBM based on GPU can get an ideal acceleration effect.
However, the research of LBM based on GPU has not been thorough enough, especially the realization and optimization of LBM in complex flow field. In addition, in the related research of intracranial aneurysms hemodynamics based on LBM, the numerical simulation of individual real aneurysms implanted with stents has not been realized. In this paper, firstly, we will develop a GPU-based LMB, and on this basis, we will launch a personalized real intracranial aneurysm hemodynamics research based on medical images.
(1) In the aspect of GPU-based LBM, firstly, taking the simple flow field such as square cavity flow as an example, this paper focuses on the role of memory access optimization and other optimization techniques, analyzes the performance of the program in detail, and discusses the factors that affect the performance of the LBM program on the GPU processor. Compared with the optimized CPU program, the maximum acceleration ratio can reach 50 times on Tesla C1060. On this basis, two algorithms based on complete matrix method and sparse matrix method are proposed for different characteristics of complex flow field, and a large number of numerical simulations are carried out to analyze their applicable conditions. The sparse matrix algorithm is selected on Tesla C1060. More than 250 MLUPS (millions of grid updates per second) are obtained, which is superior to the relevant international results (170MLUPS). Finally, the algorithm is further promoted to the multi-GPU platform to optimize the data access and communication process. On the platform equipped with four Tesla C1060 blocks, the complete matrix algorithm can achieve nearly 100% parallel efficiency and the sparse matrix algorithm. You can get close to 90% parallel efficiency.
(2) In terms of hemodynamics of intracranial aneurysms, in view of the fact that most previous studies have simplified blood as Newtonian fluid, this paper compares the results of Newtonian model and non-Newtonian model by using different aneurysm models and stents with different porosity, so as to explore the rationality of this simplified hypothesis and analyze non-Newtonian model. The results show that aneurysms with narrow carotid artery and those with low porosity stents are more susceptible to non-Newtonian effects. From this point of view, we simulate the real aneurysms individually by three-dimensional reconstruction of aneurysm model and virtual placement of stents. Relevant hemodynamic problems were studied. It was found that stent placement had a great effect on narrow carotid aneurysms and high porosity stent placement aneurysms, and the direction of blood flow into aneurysms changed significantly.
In a word, this paper advances the research of LBM based on GPU, and optimizes the simulation of complex flow field in detail. At the same time, this paper uses LBM to study the related problems of intracranial aneurysm hemodynamics, and achieves the numerical research based on the individualized real aneurysm model.
【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：R739.41
本文編號：2206981
[Abstract]:Intracranial aneurysms are a common and dangerous intracranial disease. Subarachnoid hemorrhage after rupture of intracranial aneurysms has a high morbidity and mortality rate. Stent implantation as a new treatment of intracranial aneurysms has attracted wide attention. This method has the advantages of minimally invasive, rapid recovery and good curative effect. To study the effect of aneurysm shape, stent structure and other factors, it is necessary to study the real aneurysm model in detail. One of the main factors in rupture and treatment is the use of numerical simulation to study the hemodynamic parameters in aneurysms, which is the best choice to explore the role of stents. Therefore, it is necessary to develop efficient computing methods and advanced parallel computing technology.
The mesoscopic lattice Boltzmann method (LBM), developed in the late 1980s, has the advantages of both microscopic and macroscopic models. It has many advantages over traditional methods in simulating fluid interactions and dealing with complex boundaries. It is especially suitable for studying blood flow in intracranial aneurysms. At the same time, parallel technology based on graphics processor (GPU) has developed rapidly in recent years. Compared with traditional CPU-based applications, GPU-based applications can achieve speedup of one to two orders of magnitude. LBM's natural parallelism makes it possible to speed up with GPU. With good matching, LBM based on GPU can get an ideal acceleration effect.
However, the research of LBM based on GPU has not been thorough enough, especially the realization and optimization of LBM in complex flow field. In addition, in the related research of intracranial aneurysms hemodynamics based on LBM, the numerical simulation of individual real aneurysms implanted with stents has not been realized. In this paper, firstly, we will develop a GPU-based LMB, and on this basis, we will launch a personalized real intracranial aneurysm hemodynamics research based on medical images.
(1) In the aspect of GPU-based LBM, firstly, taking the simple flow field such as square cavity flow as an example, this paper focuses on the role of memory access optimization and other optimization techniques, analyzes the performance of the program in detail, and discusses the factors that affect the performance of the LBM program on the GPU processor. Compared with the optimized CPU program, the maximum acceleration ratio can reach 50 times on Tesla C1060. On this basis, two algorithms based on complete matrix method and sparse matrix method are proposed for different characteristics of complex flow field, and a large number of numerical simulations are carried out to analyze their applicable conditions. The sparse matrix algorithm is selected on Tesla C1060. More than 250 MLUPS (millions of grid updates per second) are obtained, which is superior to the relevant international results (170MLUPS). Finally, the algorithm is further promoted to the multi-GPU platform to optimize the data access and communication process. On the platform equipped with four Tesla C1060 blocks, the complete matrix algorithm can achieve nearly 100% parallel efficiency and the sparse matrix algorithm. You can get close to 90% parallel efficiency.
(2) In terms of hemodynamics of intracranial aneurysms, in view of the fact that most previous studies have simplified blood as Newtonian fluid, this paper compares the results of Newtonian model and non-Newtonian model by using different aneurysm models and stents with different porosity, so as to explore the rationality of this simplified hypothesis and analyze non-Newtonian model. The results show that aneurysms with narrow carotid artery and those with low porosity stents are more susceptible to non-Newtonian effects. From this point of view, we simulate the real aneurysms individually by three-dimensional reconstruction of aneurysm model and virtual placement of stents. Relevant hemodynamic problems were studied. It was found that stent placement had a great effect on narrow carotid aneurysms and high porosity stent placement aneurysms, and the direction of blood flow into aneurysms changed significantly.
In a word, this paper advances the research of LBM based on GPU, and optimizes the simulation of complex flow field in detail. At the same time, this paper uses LBM to study the related problems of intracranial aneurysm hemodynamics, and achieves the numerical research based on the individualized real aneurysm model.
【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：R739.41
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