具擴展性的分散式資料儲存系統

莊哲偉

doi:10.6842/NCTU.2015.00647

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具擴展性的分散式資料儲存系統

A Scalable Storage System for Big Data Analysis

莊哲偉 , Masters　　Advisor：李鎮宜　　

英文 DOI： 10.6842/NCTU.2015.00647

拓展性；嵌入式系統；分散式運算；異質加速運算；大資料分析； scalable ； embedded system ； distributive computation ； heterogeneous computing ； Big data analysis

Abstract │ Reference (34) │ Altmetrics

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Recently, Machine learning has been widely used in various areas. Since Machine Learning is basically for Big Data Analysis which requires large amount of computation loads and storage, Machine learning will be efficiently accelerated if only if computation ability and storage equipment are both properly optimized through some methodologies. We tried to explore a hardware/software co-design platform for big data analysis with machine learning capability and storage scalability to solve the two major problems in Machine learning that is power, and Speed. To verify this concept, we built a scalable storage system on Hadoop which adopts heterogeneous architectures (CPU+FPGA) for acceleration and power reduction.

this thesis will introduce this platform's parameter setting in detail, port a well-known clustering algorithm K-means onto this platform and finally show the profiled comparison between CPU clustering and CPU+FPGA clustering in speed and power. Based on this profiling result, we can claim that this architecture really works.

This architecture is different from the solution of CPU+GPU cluster multi-core architecture proposed by Microsoft on 2009. This proposed solution also has the ability of acceleration. Another advantage of it will be of that this architecture can be implemented as the prototype of ASIC and offers a rather accurate prediction of the acceleration after taping out as a chip. Like in this platform, we implement the circuit on FPGA at 120MHz, however this same circuit can pass 200MHz test simulated at UMC 90nm technology which gives us a prediction of the speed. Also the final acceleration is around 25 times faster than not accelerated A9 CPU cluster.

Reference ( 34 ) 〈TOP〉

[3] Learning Feature Representations with K-means Montavon, G. B. Orr, K.-R. M¨uller
連結：
(Eds.), Neural Networks: Tricks of the Trade, 2nd edn, Springer LNCS 7700, 2012.
連結：
Coates1, Honglak Lee2, Andrew Y. Ng Computer Science Department, Stanford
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[5]Finding the Observed Information Matrix when Using the EM Algorithm, Thomas A.
連結：
Louis Journal of the Royal Statistical Society. Series B (Methodological)
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