發(fā)布時間：2018-05-02 16:29

  本文選題：GPU + CUDA��； 參考：《中南大學》2013年碩士論文

【摘要】：摘要：隨著地理信息系統(tǒng)技術、遙感技術、計算機技術以及信息技術的快速發(fā)展,以多種技術結合為手段,從海量的實時數據源中提取有用信息,用以解決資源管理配置、城市規(guī)劃管理、土地信息管理、生態(tài)環(huán)境管理、基礎設施建設、交通規(guī)劃等問題。然而,對于海量數據的處理,其運算量相對于一般的處理來說會有幾百到幾千倍的增加,不僅嚴重考驗著計算機的數據處理能力,而且考驗著算法設計在處理海量數據問題中的有效性。高性能計算技術突飛猛進的發(fā)展,給海量數據的處理工作帶來了新的方向。多核CPU技術以及圖形處理器(GPU)日益增強的可編程性以及高效計算能力,促進處理方式的巨大變化,由以往的CPU端編程處理,逐漸過渡到CPU+GPU異構編程處理,再發(fā)展到分布式處理,以及云計算。處理方式的改變帶動了處理效率的提高,并由以往單一的計算機到現今多臺計算機同時計算,同時實現了事務處理的均衡分配,可有效的利用各種計算機資源以提高處理效率。本文中結合GPU并行計算技術,探討如何利用GPU存儲器特點完成對海量數據的處理任務,以取得較好的加速效果。 本文所做的工作有如下幾個方面： 1、針對海量遙感影像數據的切分與調度問題,提出了基于CPU+GPU異構編程快速處理影像數據的解決方案,探討利用不同的GPU存儲器實現遙感影像數據的重采樣處理�；贕PU并行技術的影像處理,應考慮到算法的設計和處理任務的劃分,合理的劃分線程,實現并行執(zhí)行優(yōu)化、存儲器優(yōu)化、指令使用優(yōu)化,以提高整體的處理效率。闡述了統(tǒng)一計算設備架構(Compute Unified Device Architecture, CUDA)通用計算模型構架及其特點,并在此基礎上實現了對于遙感影像數據的重采樣加速。 2、提出GPU并行處理空間聚類中復雜的數值計算問題。以二部圖空間聚類算法為例,依據聚類中數值計算的特點,以及GPU并行結構和硬件特點,探討合適的并行處理方式,采用全局存儲器、共享存儲器加速技術,提高了數據的處理效率。實驗結果表明,基于GPU并行計算比CPU串行計算在效率上有顯著的提高。圖14幅,表7個,參考文獻47篇。
[Abstract]:Abstract: with the rapid development of geographic information system technology, remote sensing technology, computer technology and information technology, useful information is extracted from massive real-time data sources to solve the problem of resource management. Urban planning management, land information management, ecological environment management, infrastructure construction, traffic planning and other issues. However, for the processing of massive data, its computation will increase hundreds to thousands times compared with the normal processing, which not only seriously tests the computer's data processing ability, It also tests the effectiveness of algorithm design in dealing with massive data problems. The rapid development of high performance computing technology has brought a new direction to the processing of massive data. The increasing programmability and high efficiency computing power of multi-core CPU technology and graphics processor (GPU) promote the great change of processing methods, and gradually transition from the former CPU side programming processing to the CPU GPU heterogeneous programming processing. And then into distributed processing, and cloud computing. The change of processing mode leads to the improvement of processing efficiency, and from the former single computer to many computers at the same time, it realizes the balanced allocation of transaction processing, which can effectively use all kinds of computer resources to improve the processing efficiency. Combined with GPU parallel computing technology, this paper discusses how to use the characteristics of GPU memory to complete the processing of massive data in order to achieve a better acceleration effect. The work done in this paper is as follows: 1. Aiming at the problem of segmentation and scheduling of massive remote sensing image data, a solution of fast processing of remote sensing image data by heterogeneous programming based on CPU GPU is proposed, and different GPU memory is used to realize resampling processing of remote sensing image data. The image processing based on GPU parallel technology should consider the design of algorithm and partition of processing tasks, reasonably partition threads, realize parallel execution optimization, memory optimization, instruction usage optimization, in order to improve the overall processing efficiency. In this paper, the general computing model architecture of computer Unified Device Architecture (CUDAA) and its characteristics are described. On this basis, the resampling acceleration of remote sensing image data is realized. 2. GPU parallel processing of complex numerical computation problem in spatial clustering is proposed. Taking the bipartite graph space clustering algorithm as an example, according to the characteristics of numerical calculation in clustering, as well as the parallel structure and hardware characteristics of GPU, the suitable parallel processing method is discussed. The global memory and shared memory acceleration technology are adopted. The efficiency of data processing is improved. Experimental results show that the efficiency of parallel computing based on GPU is significantly higher than that of serial computing based on CPU. There are 14 figures, 7 tables and 47 references.
【學位授予單位】：中南大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：TP751;P208

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本文編號：1834653