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研究生： 袁帥
論文名稱： Accelerate Reed-Solomon Codes on GPUs
應用GPGPU加速Reed-Solomon Erasure Code的編解碼
指導教授： 周志遠
口試委員: 李哲榮
林俊淵
學位類別： 碩士
Master
系所名稱： 電機資訊學院 - 資訊工程學系
Computer Science
論文出版年： 2014
畢業學年度： 102
語文別： 英文
論文頁數： 47
中文關鍵詞： 里德-所羅門碼 、抹除碼 、通用圖形處理器
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    • Reed-Solomon Codes是一種在cloud storage system中被廣泛使用的redundancy solution。與replication這一傳統的redundancy solution相比，它在保證系統fault tolerance的同時，又能有效降低storage overhead。然而，Reed-Solomon Codes的編解碼複雜程度高，需要消耗大量的運算時間。在這篇論文中，我們採用GPU作爲accelerator，並探討了一些利用GPU來加速Reed-Solomon Codes編解碼的技巧。我們也用CUDA完成了GPU版本的Reed-Solomon Codes的實作，並對它的performance進行evaluate。作爲比較，我們也在Intel Xeon CPU上測試我們目前所知的最佳CPU實作——Jerasure的performance，最終，我們優化後的GPU版本可以獲得14倍以上的加速比。

    1 Introduction 1 2 Related Works 3 3 Background 5 3.1 Reed-Solomon Coding Mechanism . . . . . . . . . . . . . . . . . . . . 5 3.2 Brief Introduction of Galois Field . . . . . . . . . . . . . . . . . . . . 6 4 Accelerating Operations over Galois Field 8 4.1 GPU Implementation: Loop-based or Table-based? . . . . . . . . . . 8 4.1.1 Overview of the Loop-based Method . . . . . . . . . . . . . . 8 4.1.2 Overview of the Table-based Methods . . . . . . . . . . . . . . 9 4.1.3 Comparison between the Loop-based and the Log&exp Table- based Method . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 4.2 Further Improvement of the Log&exp Table-based Method . . . . . . 13 5 Accelerating Matrix Multiplication 17 5.1 Square-Tiling Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . 17 5.2 Generalized Tiling Algorithm . . . . . . . . . . . . . . . . . . . . . . 19 5.3 Further Improvement of Tiling Algorithm . . . . . . . . . . . . . . . . 21 6 Accelerating Decoding Matrix Generation 23 7 Reducing Data Transfer Overhead 26 7.1 Using Pinned Host Memory . . . . . . . . . . . . . . . . . . . . . . . 26 7.2 Using CUDA Streaming . . . . . . . . . . . . . . . . . . . . . . . . . 28 8 Experiment 31 8.1 Experiment Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 8.2 Evaluation Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 8.3 Overall Performance Evaluation . . . . . . . . . . . . . . . . . . . . . 32 8.3.1 Step-by-step Improvement . . . . . . . . . . . . . . . . . . . . 33 8.3.2 GPU vs. CPU . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 8.4 Accelerating Operations over Galois Field . . . . . . . . . . . . . . . 34 8.4.1 GPU Implementation: Loop-based or Table-based? . . . . . . 34 8.4.2 Further Improvement of the Log&exp Table-based Method . . 35 8.5 Accelerating Matrix Multiplication . . . . . . . . . . . . . . . . . . . 36 8.6 Reducing Data Transfer Overhead . . . . . . . . . . . . . . . . . . . . 39 8.6.1 Using Pinned Host Memory . . . . . . . . . . . . . . . . . . . 39 8.6.2 Using CUDA Streaming . . . . . . . . . . . . . . . . . . . . . 40 9 Conclusion 42

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