發(fā)布時(shí)間：2018-04-11 01:23

  本文選題：MapReduce + 高階矩陣��； 參考：《小型微型計(jì)算機(jī)系統(tǒng)》2015年12期

【摘要】：高階矩陣的存儲(chǔ)和處理在信息、經(jīng)濟(jì)、生物等學(xué)科領(lǐng)域都有十分重要的應(yīng)用,但是由于單節(jié)點(diǎn)計(jì)算機(jī)CPU、內(nèi)存等資源的限制,導(dǎo)致了對(duì)高階矩陣的處理存在一定的困難.在研究云計(jì)算平臺(tái)Hadoop及其核心組件MapReduce的基礎(chǔ)上,研究實(shí)現(xiàn)了處理高階矩陣乘法的通用并行算法(內(nèi)積法),在此基礎(chǔ)上,對(duì)內(nèi)積法進(jìn)行了改進(jìn),提出一種基于緩存的分布式并行算法(緩存法),通過(guò)實(shí)驗(yàn)仿真表明,緩存法相比內(nèi)積法執(zhí)行效率更高,不僅適合處理高階稀疏矩陣,而且可以處理高階稠密矩陣,并且在并行效果上接近理論線性加速比.
[Abstract]:The storage and processing of high order matrices are very important in the fields of information, economy, biology and so on. However, because of the limitation of resources such as single node computer CPU and memory, it is difficult to deal with higher order matrices.Based on the research of cloud computing platform Hadoop and its core component MapReduce, a general parallel algorithm (inner product method) for dealing with high order matrix multiplication is developed. On this basis, the inner product method is improved.A cache based distributed parallel algorithm (cache method) is proposed. The experimental results show that the cache method is more efficient than the inner product method, which is not only suitable for dealing with high order sparse matrix, but also can deal with high order dense matrix.And the parallel effect is close to the theoretical linear speedup.
【作者單位】： 中國(guó)地質(zhì)大學(xué)武漢計(jì)算機(jī)學(xué)院;
【基金】：國(guó)家自然科學(xué)基金青年項(xiàng)目(61305087,61402425)資助;國(guó)家自然科學(xué)基金面上項(xiàng)目(61272470)資助 中國(guó)博士后科學(xué)基金項(xiàng)目(2014M562086)資助
【分類號(hào)】：TP338.8

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相關(guān)期刊論文 前2條

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本文編號(hào)：1733815