基於CUDA平行處理H.264畫面間預測解碼器之車輛管理系統

現今車輛管理系統仍多以傳統CPU串行解碼為主，但隨著現今社會的運輸規模倍增,因此對於車輛管理系統的需求規模亦同步擴大,但該系統的設備擴充就成為龐大的問題。本文提出新型的車輛管理系統包含基本車輛管理系統功能，如行車軌跡、地圖影像對應，以及將H.264解碼瓶頸-畫面間預測動態補償的部分移植至GPU中做平行運算，並以每一鏡頭為平行處理單位，由於各鏡頭之間並無資料相依性問題，所以相當適合以GPU來做平行處理。針對車輛管理系統的兩大需求影像與座標地圖及平行化影像解碼，文中將分別介紹與實作H.264編解碼、Google Map API、CUDA平行化處理三大主題。本實驗使用個人電腦PC平臺，由於主機端與設備端記憶體傳輸瓶頸，及平行運算Block規模數量規劃不足，雖然單就動態補償部分的實驗隨處理鏡頭數量上升至8-16個鏡頭時，效能上升12%-29%，但在整體車輛管理系統表現，仍與純粹使用CPU解碼效果相差無幾，並無顯著提升。

關鍵字

車輛管理系統；平行處理； H.264 解碼器

並列摘要

Most of today's vehicle management system is still dominated by the traditional CPU serial decoding, however, along with the multiplication of transportation scale, the demand for vehicle management system will simultaneously to expand, so how to expand this system equipment will become a huge of problems. This paper presents a new type of vehicle management system includes the basic vehicle management system functions such as Channel track on map, the corresponding map image, and transplanted H.264 inter-frame motion compensation prediction part to the GPU to do parallel processing. Since there is no data dependency between each Channel problems, so do quite suitable for GPU parallel processing each channel. The demand for vehicle management system respectively will be introduced with the implementation of H.264 codec, Google Map API and CUDA parallel processing. In this study, due to the memory transmission bottlenecks between host side and device-side. And the inadequate planning number of scale parallel computing Block, The performance of experimental motion compensation will increase 12%-29% while handing channel number rise to 8-16 lens. However the overall performance of the vehicle management system is no significant improvement of using the CPU decoding.

並列關鍵字

Vehicle Management System ； Parallel Processing ； H.264 Decoder

參考文獻

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[1] New video codec to ease pressure on global networks Successor to award-winning standard to unleash new innovation[Online]. Available:http://www.itu.int/net/pressoffice/press_releases/2013/01.aspx#.UUR9rRzQels.