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PnetCDF is a library providing high-performance parallel I/O while still maintaining file-format compatibility with Unidata's NetCDF, specifically the formats of CDF-1 and CDF-2. Although NetCDF supports parallel I/O starting from version 4, the files must be in HDF5 format. PnetCDF is currently the only choice for carrying out parallel I/O on files that are in classic formats (CDF-1 and 2). In addition, PnetCDF supports the CDF-5 file format, an extension of CDF-2, that supports more data types and allows users to define large dimensions, attributes, and variables (>2B elements). NetCDF gives scientific programmers a self-describing and portable means for storing data. However, prior to version 4, netCDF does so in a serial manner. By making some small changes to the netCDF APIs, PnetCDF can use MPI-IO to achieve high-performance parallel I/O. [1]


bib[ title = “Parallel netCDF: A High-Performance Scientific I/O Interface” authors = “Jianwei Li, Wei-keng Liao, Alok Choudhary, Robert Ross, Rajeev Thakur, William Gropp” year = “2003” booktitle = “SC '03: Proceedings of the 2003 ACM/IEEE Conference on Supercomputing” publisher = “IEEE” isbn = “1-58113-695-1” doi = “” groups = “research/us-anl” keywords = “PNetCDF, Middleware” abstract = “Dataset storage, exchange, and access play a critical role in scientific applications. For such purposes netCDF serves as a portable, efficient file format and programming interface, which is popular in numerous scientific application domains. However, the original interface does not provide an efficient mechanism for parallel data storage and access. In this work, we present a new parallel interface for writing and reading netCDF datasets. This interface is derived with minimal changes from the serial netCDF interface but defines semantics for parallel access and is tailored for high performance. The underlying parallel I/O is achieved through MPI-IO, allowing for substantial performance gains through the use of collective I/O optimizations. We compare the implementation strategies and performance with HDF5. Our tests indicate programming convenience and significant I/O performance improvement with this parallel netCDF (PnetCDF) interface.” ]