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  1. Proceedings of the 1st Joint International Workshop on Parallel Data Storage & Data Intensive Scalable Computing Systems (PDSW-DISCS '16)
  2. A bloom filter based scalable data integrity check tool for large-scale dataset
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Scientific workflows at datawarp-speed: accelerated data-intensive science using NERSC's burst buffer
Parallel I/O characterisation based on server-side performance counters
Replicating HPC I/O workloads with proxy applications
Can non-volatile memory benefit mapreduce applications on HPC clusters?
Fatman vs. littleboy: scaling up linear algebraic operations in scale-out data platforms
Klimatic: a virtual data lake for harvesting and distribution of geospatial data
Get out of the way!: applying compression to internal data structures
Towards energy efficient data management in HPC: the open ethernet drive approach
A generic framework for testing parallel file systems
A bloom filter based scalable data integrity check tool for large-scale dataset

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A bloom filter based scalable data integrity check tool for large-scale dataset

Content Provider ACM Digital Library
Author Xiong, Sisi Wang, Feiyi Cao, Qing
Abstract Large scale HPC applications are becoming increasingly data intensive. At Oak Ridge Leadership Computing Facility (OLCF), we are observing the number of files curated under individual project are reaching as high as 200 millions and project data size is exceeding petabytes. These simulation datasets, once validated, often needs to be transferred to archival system for long term storage or shared with the rest of the research community. Ensuring the data integrity of the full dataset at this scale is paramount important but also a daunting task. This is especially true considering that most conventional tools are serial and file-based, unwieldy to use and/or can't scale to meet user's demand. To tackle this particular challenge, this paper presents the design, implementation and evaluation of a scalable parallel checksumming tool, fsum, which we developed at OLCF. It is built upon the principle of parallel tree walk and work-stealing pattern to maximize parallelism and is capable of generating a single, consistent signature for the entire dataset at extreme scale. We also applied a novel bloom-filter based technique in aggregating signatures to overcome the signature ordering requirement. Given the probabilistic nature of bloom filter, we provided a detailed error and trade-off analysis. Using multiple datasets from production environment, we demonstrated that our tool can efficiently handle both very large files as well as many small-file based datasets. Our preliminary test showed that on the same hardware, it outperforms conventional tool by as much as 4x. It also exhibited near-linear scaling properties when provisioned with more compute resources.
Starting Page 55
Ending Page 60
Page Count 6
ISBN 9781509052165
DOI 10.1109/PDSW-DISCS.2016.13
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2016-11-13
Access Restriction Subscribed
Content Type Text
Resource Type Article
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