Comparative Evaluation of Big-Data Systems on Scientific Image Analytics Workloads

被引：29

作者：

Mehta, Parmita ^{[1
]}

Dorkenwald, Sven ^{[1
]}

Zhao, Dongfang ^{[1
]}

Kaftan, Tomer ^{[1
]}

Cheung, Alvin ^{[1
]}

Balazinska, Magdalena ^{[1
]}

Rokem, Ariel ^{[1
]}

Connolly, Andrew ^{[1
]}

Vanderplas, Jacob ^{[1
]}

AlSayyad, Yusra ^{[1
]}

机构：

[1] Univ Washington, Seattle, WA 98195 USA

来源：

PROCEEDINGS OF THE VLDB ENDOWMENT | 2017年 / 10卷 / 11期

基金：

美国国家科学基金会;

关键词：

D O I：

10.14778/3137628.3137634

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Scientific discoveries are increasingly driven by analyzing large volumes of image data. Many new libraries and specialized database management systems (DBMSs) have emerged to support such tasks. It is unclear how well these systems support real-world image analysis use cases, and how performant the image analytics tasks implemented on top of such systems are. In this paper, we present the first comprehensive evaluation of large-scale image analysis systems using two real-world scientific image data processing use cases. We evaluate five representative systems (SciDB, Myria, Spark, Dask, and TensorFlow) and find that each of them has shortcomings that complicate implementation or hurt performance. Such shortcomings lead to new research opportunities in making large-scale image analysis both efficient and easy to use.

引用

页码：1226 / 1237

页数：12

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