Many scientific applications operate on data sets that span hundreds of Gigabytes or even Terabytes in size. Large data sets often use compression to reduce the size of the files. Yet as of today, parallel I/O libraries do not support reading and writing compressed files, necessitating either expensive sequential compression/decompression operations before/after the simulation, or omitting advanced features of parallel I/O libraries, such as collective I/O operations. This paper introduces parallel I/O on compressed data files, discusses the key challenges, requirements, and solutions for supporting compressed data files in MPI I/O, as well as limitations on some MPI I/O operations when using compressed data files. The paper details handling of individual read and write operations of compressed data files, and presents an extension to the two-phase collective I/O algorithm to support data compression. The paper further presents and evaluates an implementation based on the Snappy compression library and the OMPIO parallel I/O framework. The performance evaluation using multiple data sets demonstrate significant performance benefits when using data compression on a parallel BeeGFS file system.
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MPI-Vector-IO: Parallel I/O and Partitioning for Geospatial Vector Data
In recent times, geospatial datasets are growing in terms of size, complexity and heterogeneity. High performance systems are needed to analyze such data to produce actionable insights in an efficient manner. For polygonal a.k.a vector datasets, operations such as I/O, data partitioning, communication, and load balancing becomes challenging in a cluster environment. In this work, we present MPI-Vector-IO, a parallel I/O library that we have designed using MPI-IO specifically for partitioning and reading irregular vector
data formats such as Well Known Text. It makes MPI aware of spatial data, spatial primitives and provides support for spatial data types embedded within collective computation and communication using MPI message-passing library. These abstractions along with
parallel I/O support are useful for parallel Geographic Information System (GIS) application development on HPC platforms. Performance evaluation is done on Lustre and GPFS filesystems. MPI-Vector-IO scales well with MPI processes and file size and
achieves bandwidth up to 22 GB/s for common spatial data access patterns. We observed that independent file read functions performed better than collective functions in MPI-IO for contiguous access pattern on Lustre. In general, the I/O is improved by one to two orders of magnitude over real-world datasets using up to 1152 CPU cores. Spatial Join query is used as an exemplar to demonstrate an end-to-end application using MPI-Vector-IO.
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- Award ID(s):
- 1756000
- NSF-PAR ID:
- 10088009
- Date Published:
- Journal Name:
- Proceedings of the 47th International Conference on Parallel Processing (ICPP 2018).
- Page Range / eLocation ID:
- 1 to 11
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3225058.3225105
