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  1. Proceedings of the 2013 international symposium on memory management (ISMM '13)
  2. A bloat-aware design for big data applications
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Safety-first approach to memory consistency models
Towards hinted collection: annotations for decreasing garbage collector pause times
Generating sound and effective memory debuggers
Precise and scalable context-sensitive pointer analysis via value flow graph
Adaptive scanning reduces sweep time for the Lisp2 mark-compact garbage collector
Rigorous benchmarking in reasonable time
Analyzing memory ownership patterns in C libraries
Control theory for principled heap sizing
ACDC: towards a universal mutator for benchmarking heap management systems
Elephant tracks: portable production of complete and precise gc traces
Pacman: program-assisted cache management
A bloat-aware design for big data applications

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A bloat-aware design for big data applications

Content Provider ACM Digital Library
Author Borkar, Vinayak Carey, Michael J. Xu, Guoqing Bu, Yingyi
Abstract Over the past decade, the increasing demands on data-driven business intelligence have led to the proliferation of large-scale, data-intensive applications that often have huge amounts of data (often at terabyte or petabyte scale) to process. An object-oriented programming language such as Java is often the developer's choice for implementing such applications, primarily due to its quick development cycle and rich community resource. While the use of such languages makes programming easier, significant performance problems can often be seen --- the combination of the inefficiencies inherent in a managed run-time system and the impact of the huge amount of data to be processed in the limited memory space often leads to memory bloat and performance degradation at a surprisingly early stage. This paper proposes a bloat-aware design paradigm towards the development of efficient and scalable Big Data applications in object-oriented GC enabled languages. To motivate this work, we first perform a study on the impact of several typical memory bloat patterns. These patterns are summarized from the user complaints on the mailing lists of two widely-used open-source Big Data applications. Next, we discuss our design paradigm to eliminate bloat. Using examples and real-world experience, we demonstrate that programming under this paradigm does not incur significant programming burden. We have implemented a few common data processing tasks both using this design and using the conventional object-oriented design. Our experimental results show that this new design paradigm is extremely effective in improving performance --- even for the moderate-size data sets processed, we have observed 2.5x+ performance gains, and the improvement grows substantially with the size of the data set.
Starting Page 119
Ending Page 130
Page Count 12
File Format PDF
ISBN 9781450321006
DOI 10.1145/2464157.2466485
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2013-06-20
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Design Big data applications Memory bloat
Content Type Text
Resource Type Article
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