| Item type |
SIG Technical Reports(1) |
| 公開日 |
2014-12-02 |
| タイトル |
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タイトル |
Performance Analysis of MapReduce Implementations for High Performance Homology Search (Unrefereed Workshop Manuscript) |
| タイトル |
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言語 |
en |
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タイトル |
Performance Analysis of MapReduce Implementations for High Performance Homology Search (Unrefereed Workshop Manuscript) |
| 言語 |
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言語 |
eng |
| キーワード |
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主題Scheme |
Other |
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主題 |
性能評価 |
| 資源タイプ |
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_18gh |
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資源タイプ |
technical report |
| 著者所属 |
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Tokyo Institute of Technology |
| 著者所属 |
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Tokyo Institute of Technology／JST CREST |
| 著者所属 |
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Tokyo Institute of Technology |
| 著者所属 |
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Tokyo Institute of Technology／JST CREST |
| 著者所属 |
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Tokyo Institute of Technology／JST CREST |
| 著者所属(英) |
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en |
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Tokyo Institute of Technology |
| 著者所属(英) |
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en |
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Tokyo Institute of Technology / JST CREST |
| 著者所属(英) |
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en |
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Tokyo Institute of Technology |
| 著者所属(英) |
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en |
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Tokyo Institute of Technology / JST CREST |
| 著者所属(英) |
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en |
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Tokyo Institute of Technology / JST CREST |
| 著者名 |
Chaojie, Zhang
Koichi, Shirahata
Shuji, Suzuki
Yutaka, Akiyama
Satoshi, Matsuoka
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| 著者名(英) |
Chaojie, Zhang
Koichi, Shirahata
Shuji, Suzuki
Yutaka, Akiyama
Satoshi, Matsuoka
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| 論文抄録 |
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内容記述タイプ |
Other |
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内容記述 |
Homology search to be used in emerging bioinformatics problems such as metagenomics is of increasing importance and challenge as its application area grows more broadly while the computational complexity is increasing, thus requiring massive parallel data processing. Earlier work by some of the authors have devised novel algorithms such as GHOSTX, but the master-worker parallelization to enumerate and schedule for data processing was done with a privately developed, MPI-based master-worker framework called GHOST-MP. An alternative is to utilize the now-popular big data software substrates, such as MapReduce with abundant associated software tool-chains, but it is not clear whether the massive resource required by metagenomic homology search would not overwhelm its known limitations. By converting the GHOST-MP master-worker data processing pipeline to accommodate MapReduce, and benchmarking them on a variety of high-performance MapReduce incarnations including Hadoop and Spark, we attempt to characterize the appropriateness of MapReduce as a generic framework for metagenomics that embody extremely resource consuming requirements for both compute and data. |
| 論文抄録(英) |
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内容記述タイプ |
Other |
|
内容記述 |
Homology search to be used in emerging bioinformatics problems such as metagenomics is of increasing importance and challenge as its application area grows more broadly while the computational complexity is increasing, thus requiring massive parallel data processing. Earlier work by some of the authors have devised novel algorithms such as GHOSTX, but the master-worker parallelization to enumerate and schedule for data processing was done with a privately developed, MPI-based master-worker framework called GHOST-MP. An alternative is to utilize the now-popular big data software substrates, such as MapReduce with abundant associated software tool-chains, but it is not clear whether the massive resource required by metagenomic homology search would not overwhelm its known limitations. By converting the GHOST-MP master-worker data processing pipeline to accommodate MapReduce, and benchmarking them on a variety of high-performance MapReduce incarnations including Hadoop and Spark, we attempt to characterize the appropriateness of MapReduce as a generic framework for metagenomics that embody extremely resource consuming requirements for both compute and data. |
| 書誌レコードID |
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収録物識別子タイプ |
NCID |
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収録物識別子 |
AN10096105 |
| 書誌情報 |
研究報告計算機アーキテクチャ（ARC）
巻 2014-ARC-213,
号 29,
p. 1-7,
発行日 2014-12-02
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| Notice |
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SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc. |
| 出版者 |
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言語 |
ja |
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出版者 |
情報処理学会 |
IPSJ-ARC14213029.pdf (1.0 MB)
