Comparative Evaluation of Big-Data Systems on Scientific Image Analytics Workloads
Comparative Evaluation of Big-Data Systems on Scientific Image Analytics Workloads
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DOI：
10.14778/3137628.3137634
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发表时间：
2016-12
期刊：
影响因子：
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通讯作者：
Parmita Mehta;Sven Dorkenwald;Dongfang Zhao;Tomer Kaftan;Alvin Cheung;M. Balazinska;A. Rokem;A. Connolly;J. Vanderplas;Y. AlSayyad
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文献类型：
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作者：
Parmita Mehta;Sven Dorkenwald;Dongfang Zhao;Tomer Kaftan;Alvin Cheung;M. Balazinska;A. Rokem;A. Connolly;J. Vanderplas;Y. AlSayyad
科学发现越来越多地通过分析大量图像数据来推动。许多新的库和专门的数据库管理系统（DBMS）已经出现，以支持这些任务。目前还不清楚这些系统对真实图像分析用例的支持程度，以及在这些系统上实现的图像分析任务的性能如何。在本文中，我们使用两个真实世界的科学图像数据处理用例对大型图像分析系统进行了首次全面评估。我们评估了五个代表性的系统（SciDB，Myria，Spark，Dask和TensorFlow），发现每个系统都有使实现复杂化或损害性能的缺点。这些缺点导致新的研究机会，使大规模图像分析既有效又易于使用。
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.