【摘要】：目前處理器持續(xù)增長(zhǎng)的溫度已經(jīng)成為芯片設(shè)計(jì)的主要瓶頸之一。求解熱方程最常用的方法是有限差分法(Finite Difference Method,簡(jiǎn)稱(chēng)FDM)和有限元法(Finite Element Method,簡(jiǎn)稱(chēng)FEM),這兩種方法可以對(duì)物理系統(tǒng)進(jìn)行詳細(xì)的建模。但是使用有限差分法(FDM)和有限元法(FEM)對(duì)微處理器溫度進(jìn)行建模時(shí)需要大量的節(jié)點(diǎn),這也意味著需要更長(zhǎng)的計(jì)算時(shí)間。 ATMI模擬器對(duì)熱方程的求解是采用最早的分析法(Analytical Method,簡(jiǎn)稱(chēng)AM),采用分析方法的優(yōu)點(diǎn)是減少了計(jì)算所需要的節(jié)點(diǎn),在一定程度上加快了計(jì)算速度。ATMI模擬器具有高效性,可靠性和易用性,這些優(yōu)點(diǎn)使其在科研領(lǐng)域得到了廣泛的應(yīng)用。實(shí)驗(yàn)表明,當(dāng)ATMI模擬器在模擬十幾個(gè)核的處理器溫度的時(shí)候,熱響應(yīng)階段的計(jì)算需要一個(gè)周的時(shí)間。那么,隨著科學(xué)技術(shù)的不斷發(fā)展和處理器的更新?lián)Q代,ATMI模擬器已經(jīng)不適合未來(lái)處理器溫度的模擬。 本文采用CUDA技術(shù)完成了ATMI模擬器GPU平臺(tái)移植的前期工作,主要包括以下幾個(gè)方面的工作：首先,在深入研究ATMI模擬器串行算法的基礎(chǔ)上,重點(diǎn)研究了ATMI模擬器熱響應(yīng)階段的算法,結(jié)合GPU體系結(jié)構(gòu)和CUDA編程的特點(diǎn),對(duì)原有的熱響應(yīng)階段的串行算法進(jìn)行了優(yōu)化,優(yōu)化的目的是使其更合適GPU編程。其次,實(shí)現(xiàn)了GPU平臺(tái)的積分算法和貝塞爾算法。由于線(xiàn)程的并行度不夠和復(fù)雜的程序結(jié)構(gòu),實(shí)現(xiàn)的這兩個(gè)算法與原有串行算法相比實(shí)驗(yàn)結(jié)果不是很理想。在GPU平臺(tái)上求解熱響應(yīng)方程的時(shí)候可以直接調(diào)用這兩個(gè)已經(jīng)在GPU實(shí)現(xiàn)的庫(kù)函數(shù)。最后,我們提出了GPU平臺(tái)熱響應(yīng)階段的并行算法,完成了整個(gè)并行算法的框架搭建。本文從任務(wù)級(jí)進(jìn)行了并行程序的設(shè)計(jì),每一個(gè)線(xiàn)程負(fù)責(zé)一對(duì)熱源之間的計(jì)算。 本文致力于GPU平臺(tái)的模擬器的研究與探索,希望解決GPU多線(xiàn)程應(yīng)用程序在開(kāi)發(fā)過(guò)程中遇到的問(wèn)題,也希望為以后多核處理溫度相關(guān)問(wèn)題的研究提供一些可以積累的經(jīng)驗(yàn)。
[Abstract]:At present, the continuous increase of processor temperature has become one of the main bottlenecks of chip design. The most commonly used methods for solving thermal equations are finite difference method (Finite Difference Method,) and finite element method (Finite Element Method,) and finite element method (FEM), which can be used to model the physical system in detail. But using finite difference method (FDM) and finite element method (FEM) to model microprocessor temperature requires a lot of nodes. This also means longer computational time. The ATMI simulator solves the thermal equation by using the earliest analytical method, (Analytical Method, for short, and AM), which has the advantage of reducing the number of nodes needed for calculation. To some extent, ATMI simulator has high efficiency, reliability and ease of use, which makes it widely used in the field of scientific research. The experimental results show that when the ATMI simulator simulates the processor temperature of a dozen cores, the calculation of the thermal response phase takes a week. So, with the development of science and technology and the upgrading of processor, ATMI simulator is not suitable for the simulation of processor temperature in the future. In this paper, the previous work of transplanting GPU platform of ATMI simulator is accomplished by using CUDA technology. The main work includes the following aspects: firstly, on the basis of deeply studying the serial algorithm of ATMI simulator, The algorithm of the thermal response phase of ATMI simulator is studied emphatically. Combining with the characteristics of GPU architecture and CUDA programming, the serial algorithm of the original thermal response stage is optimized. The purpose of the optimization is to make it more suitable for GPU programming. Secondly, the integration algorithm and Bessel algorithm of GPU platform are implemented. Due to the insufficient parallelism of threads and the complex program structure, the experimental results of these two algorithms are not ideal compared with the original serial algorithms. These two library functions which have been implemented in GPU can be directly called when solving the thermal response equation on the GPU platform. Finally, we propose a parallel algorithm in the thermal response phase of GPU platform, and complete the framework of the whole parallel algorithm. In this paper, a parallel program is designed at the task level. Each thread is responsible for the calculation of a pair of heat sources. This paper is devoted to the research and exploration of the simulator of GPU platform, hoping to solve the problems encountered in the development of GPU multithreaded application program, and also hope to provide some accumulated experience for the research of multi-core processing temperature related problems in the future.
【學(xué)位授予單位】：內(nèi)蒙古大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類(lèi)號(hào)】：TP332

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本文編號(hào)：2227042