This document summarizes HDF software activities in 2002, including support and funding sources for HDF, recent and upcoming releases of HDF4 and HDF5 libraries and tools, and other HDF-related projects. The HDF5 library saw improvements to performance, compilers supported, and tools like HDFView and converters. The next major HDF5 release in 2003 will focus on new features, performance enhancements, and special platform support. High level APIs and the parallel HDF5 programming model were also under development.
This document summarizes Mike Folk's presentation at the Science Data Processing Workshop from February 26-28, 2002. The presentation provided updates on HDF4 and HDF5, including recent releases and future plans. HDF4 and HDF5 are open source data formats and software libraries for scientific data that support efficient storage of arrays, images, and tables. The presentation outlined ongoing work to improve performance, add new features, and facilitate the transition from HDF4 to HDF5.
The document provides an update on the HDF software projects. It discusses recent releases of HDF4, HDF5, and HDF Java products. It highlights new features, platforms supported, and organizations contributing to development. Upcoming work includes improvements to parallel I/O, data indexing and viewing tools, and harmonization with netCDF and OPeNDAP formats.
Mike Folk from the National Center for Supercomputing Applications gave an update on HDF software in 2003. HDF is supported by several government agencies for applications in earth science, simulations, and data-intensive computing. Version 4.2 Release 1 was planned for October 2003 with bug fixes and new features. HDF5 1.6.0 was released in July 2003 with new filters, properties, and performance improvements. Work was also being done on high-level APIs, parallel HDF5, tools, and collaborations with other projects.
Update on HDF, including recent changes to the software, upcoming releases, collaborations, future plans. Will include an overview of the upcoming HDF5 1.8 release, and updates on the netCDF4/HDF5 merge, HDF5 support for indexing, BioHDF, the HDF5-Storage Resource Broker project, and the HDF spin-off THG.
The document summarizes updates from The HDF Group. It discusses that The HDF Group was established in 1988 and owns HDF4 and HDF5 formats and libraries. It provides services like helpdesk, support, consulting and training to users. The HDF Group aims to ensure long-term accessibility of HDF data through development and support of HDF technologies. Recent improvements include new HDF5 and HDF4 releases, tools updates, HDF-Java and SWMR file access work. Future work involves parallel I/O, indexing methods and EOS, OPeNDAP and NPP/NPOESS support.
This document summarizes activities related to the HDF project. It discusses the status of The HDF Group as a nonprofit organization dedicated to supporting HDF. It reviews ESDIS activities including maintenance of HDF and HDF-EOS code and user support. It provides statistics on downloads, helpdesk requests, and forum usage. It also outlines maintenance and testing of HDF4, HDF5 and related software releases. Platform support issues are also addressed. The document covers recent improvements and previews future work for the HDF software suite.
This document provides an overview and update on HDF5 and its ecosystem. Key points include:
- HDF5 1.12.0 was recently released with new features like the Virtual Object Layer and external references.
- The HDF5 library now supports accessing data in the cloud using connectors like S3 VFD and REST VOL without needing to modify applications.
- Projects like HDFql and H5CPP provide additional interfaces for querying and working with HDF5 files from languages like SQL, C++, and Python.
- The HDF5 community is moving development to GitHub and improving documentation resources on the HDF wiki site.
This document summarizes Mike Folk's presentation at the Science Data Processing Workshop from February 26-28, 2002. The presentation provided updates on HDF4 and HDF5, including recent releases and future plans. HDF4 and HDF5 are open source data formats and software libraries for scientific data that support efficient storage of arrays, images, and tables. The presentation outlined ongoing work to improve performance, add new features, and facilitate the transition from HDF4 to HDF5.
The document provides an update on the HDF software projects. It discusses recent releases of HDF4, HDF5, and HDF Java products. It highlights new features, platforms supported, and organizations contributing to development. Upcoming work includes improvements to parallel I/O, data indexing and viewing tools, and harmonization with netCDF and OPeNDAP formats.
Mike Folk from the National Center for Supercomputing Applications gave an update on HDF software in 2003. HDF is supported by several government agencies for applications in earth science, simulations, and data-intensive computing. Version 4.2 Release 1 was planned for October 2003 with bug fixes and new features. HDF5 1.6.0 was released in July 2003 with new filters, properties, and performance improvements. Work was also being done on high-level APIs, parallel HDF5, tools, and collaborations with other projects.
Update on HDF, including recent changes to the software, upcoming releases, collaborations, future plans. Will include an overview of the upcoming HDF5 1.8 release, and updates on the netCDF4/HDF5 merge, HDF5 support for indexing, BioHDF, the HDF5-Storage Resource Broker project, and the HDF spin-off THG.
The document summarizes updates from The HDF Group. It discusses that The HDF Group was established in 1988 and owns HDF4 and HDF5 formats and libraries. It provides services like helpdesk, support, consulting and training to users. The HDF Group aims to ensure long-term accessibility of HDF data through development and support of HDF technologies. Recent improvements include new HDF5 and HDF4 releases, tools updates, HDF-Java and SWMR file access work. Future work involves parallel I/O, indexing methods and EOS, OPeNDAP and NPP/NPOESS support.
This document summarizes activities related to the HDF project. It discusses the status of The HDF Group as a nonprofit organization dedicated to supporting HDF. It reviews ESDIS activities including maintenance of HDF and HDF-EOS code and user support. It provides statistics on downloads, helpdesk requests, and forum usage. It also outlines maintenance and testing of HDF4, HDF5 and related software releases. Platform support issues are also addressed. The document covers recent improvements and previews future work for the HDF software suite.
This document provides an overview and update on HDF5 and its ecosystem. Key points include:
- HDF5 1.12.0 was recently released with new features like the Virtual Object Layer and external references.
- The HDF5 library now supports accessing data in the cloud using connectors like S3 VFD and REST VOL without needing to modify applications.
- Projects like HDFql and H5CPP provide additional interfaces for querying and working with HDF5 files from languages like SQL, C++, and Python.
- The HDF5 community is moving development to GitHub and improving documentation resources on the HDF wiki site.
The tool takes HDF-EOS 5 data as input, and generates COARDS-compatible output - if the input file has enough metadata to be COARDS-compliant, the output file will be COARDS-compliant. The tool is written in portable C, and ought to run on any platform where the HDF-EOS and netCDF libraries are available.
This year, we have made two major enhancements to the converter:
It now automatically detects whether its input is HDF-EOS2 or HDF-EOS5 format, and handles either one. The previous tool worked with HDF-EOS5 only.
Its netCDF output attempts to conform to the new CF conventions (a superset of the COARDS conventions). This is primarily an improvement in its translation of Swath datasets, which CF handles much better than COARDS.
This document summarizes a presentation about the current status and future directions of the Hierarchical Data Format (HDF) software. It provides updates on recent HDF5 releases, development efforts including new compression methods and ways to access HDF5 data, and outreach resources. It concludes by inviting the audience to share wishes for future HDF development.
The 2011 ACSI Survey Summary document provides results from NASA's 2011 survey of Earth science data users. Some key findings include:
- Customer satisfaction with NASA EOSDIS was steady at 77 for the fourth year in a row, meeting data users' needs.
- The most commonly used data format was HDF-EOS/HDF, though not all users found it easy to use.
- Comments from users provided both positive feedback and suggestions for improving areas like search functionality, order processing, and format preferences.
This document discusses user analysis of the HDFEOS.org website and plans for future improvements. It finds that the majority of the site's 100 daily users are "quiet", not posting on forums or other interactive elements. The main user types are locators, who search for examples or data; mergers, who combine or mosaic datasets; and converters, who change file formats. The document outlines recent updates focused on these user types, like adding Python examples for subsetting and calculating latitude and longitude. It proposes future work on artificial intelligence/machine learning uses of HDF files and examples for processing HDF data in the cloud.
This document summarizes MathWorks' work to modernize MATLAB's support for HDF5. Key points include:
1) MATLAB now supports HDF5 1.10.7 features like single-writer/multiple-reader access and virtual datasets through new and updated low-level functions.
2) Performance benchmarks show some improvements but also regressions compared to the previous HDF5 version, and work continues to optimize code and support future versions.
3) There are compatibility considerations for Linux filter plugins, but interim solutions are provided until MathWorks can ship a single HDF5 version.
The document discusses migrating from HDF5 1.6 to HDF5 1.8. It provides an overview of new features in HDF5 1.8, including a revised file format, improvements to group storage, new link types like external links, and enhanced error handling. The document recommends helping with the transition to HDF5 1.8 by discussing beneficial new features and awareness of compatibility issues when moving from 1.6 to 1.8.
This tutorial is designed for new HDF5 users. We will go over a brief history of HDF and HDF5 software, and will cover basic HDF5 Data Model objects and their properties; we will give an overview of the HDF5 Libraries and APIs, and discuss the HDF5 programming model. Simple C and Fortran examples, and Java tool HDFView will be used to illustrate HDF5 concepts.
In this talk we will discuss caching and buffering strategies in HDF5. The information presented will help developers write more efficient applications and avoid performance bottlenecks.
The document discusses recent and upcoming improvements to parallel HDF5 for improved I/O performance on HPC systems. Recent improvements include reducing file truncations, distributing metadata writes across processes, and improved selection matching. Upcoming work includes a high-level HPC API, funding for Exascale-focused enhancements, and future improvements like asynchronous I/O and auto-tuning to parallel file systems. Performance tips are also provided like passing MPI hints and using collective I/O.
The document discusses HDF and netCDF data support in ArcGIS. It provides an overview of how HDF and netCDF data can be directly ingested and used as raster datasets, mosaic datasets, feature layers, and tables in ArcGIS. This allows for scientific data to be displayed, analyzed, and shared using common GIS tools and services. It also describes existing Python tools for working with netCDF data and outlines future areas of development, including improved support for HDF5, THREDDS/OPeNDAP access, and evolving data standards.
The document describes H5Coro, a new C++ library for reading HDF5 files from cloud storage. H5Coro was created to optimize HDF5 reading for cloud environments by minimizing I/O operations through caching and efficient HTTP requests. Performance tests showed H5Coro was 77-132x faster than the previous HDF5 library at reading HDF5 data from Amazon S3 for NASA's SlideRule project. H5Coro supports common HDF5 elements but does not support writing or some complex HDF5 data types and messages to focus on optimized read-only performance for time series data stored sequentially in memory.
In this presentation, we will give an update on the HDF OPeNDAP project. We will update the new features inside the HDF5 OPeNDAP data handler. We will also introduce the enhanced HDF4 OPeNDAP data handler and demonstrate how it can help users to view and analyze remote HDF-EOS2 data. A demo that uses OPeNDAP client tools to handle AIRS and MODIS Grid/Swath data with the enhanced handler will be presented.
This document compares different methods for accessing HDF and netCDF files stored on Amazon S3, including Apache Drill, THREDDS Data Server (TDS), and HDF5 Virtual File Driver (VFD). A benchmark test of accessing a 24GB HDF5/netCDF-4 file on S3 from Amazon EC2 found that TDS performed the best, responding within 2 minutes, while Apache Drill failed after 7 minutes. The document concludes that TDS 5.0 is the clear winner based on performance and support for role-based access control and HDF4 files, but the best solution depends on use case and software.
The document describes the HDF Product Designer software tool. It was created to facilitate the design of interoperable scientific data products in HDF5 format. The tool allows intuitive editing of HDF5 objects and supports conventions like CF and ACDD. It also provides validation services to test file compliance. The goal is to help scientists design data products that follow standards and are easy for others to use.
HDF5 and Zarr are data formats that can be used to store and access scientific data. This presentation discusses approaches to translating between the two formats. It describes how HDF5 files were translated to the Zarr format by creating a separate Zarr store to hold HDF5 file chunks, and storing chunk location metadata. It also discusses an implementation that translates Zarr data to the HDF5 format by using a special chunking layout and storing chunk information in an HDF5 compound dataset. Limitations of the translations include lack of support for some HDF5 dataset properties in Zarr, and lack of support for some Zarr compression methods in the HDF5 implementation.
NetCDF and HDF5 are data formats and software libraries used for scientific data. NetCDF began in 1989 and allows for array-oriented data with dimensions, variables, and attributes. NetCDF-4 introduced new features while maintaining backward compatibility. It uses HDF5 for data storage and can read HDF4/HDF5 files. NetCDF provides APIs for C, Fortran, Java, and is widely used for earth science and climate data. It supports conventions, parallel I/O, and reading many data formats.
This document discusses three approaches to integrating HDF5 data with databases: I) Using PyTables to treat HDF5 like a database, II) Using HDF5 and a relational database side-by-side, and III) Fully integrating HDF5 and a relational database where the HDF5 data model is exposed to SQL and other database technologies.
A preponderance of data from NASA's Earth Observing System (EOS) is archived in the HDF Version 4 (HDF4) format. The long-term preservation of these data is critical for climate and other scientific studies going many decades into the future. HDF4 is very effective for working with the large and complex collection of EOS data products. Unfortunately, because of the complex internal byte layout of HDF4 files, future readability of HDF4 data depends on preserving a complex software library that can interpret that layout. Having a way to access HDF4 data independent of a library could improve its viability as an archive format, and consequently give confidence that HDF4 data will be readily accessible forever, even if the HDF4 library is gone.
To address the need to simplify long-term access to EOS data stored in HDF4, a collaborative project between The HDF Group and NASA Earth Science Data Centers is implementing an approach to accessing data in HDF4 files based on the use of independent maps that describe the data in HDF4 files and tools that can use these maps to recover data from those files. With this approach, relatively simple programs will be able to extract the data from an HDF4 file, bypassing the need for the HDF4 library.
A demonstration project has shown that this approach is feasible. This involved an assessment of NASA�s HDF4 data holdings, and development of a prototype XML-based layout mapping language and tools to read layout maps and read HDF4 files using layout maps. Future plans call for a second phase of the project, in which the mapping tools and XML schema are made production quality, the mapping schema are integrated with existing XML metadata files in several data centers, and outreach activities are carried out to encourage and facilitate acceptance of the technology.
The document discusses the HDF4 Mapping Project which aims to ensure long-term access to Earth Observing System (EOS) data stored in HDF4 files. It provides an overview of the project scope, including developing a proof of concept prototype and production quality mapping tools. It also describes verification studies conducted with NASA data centers to identify requirements for verifying correctness of HDF4 file content maps produced by the mapping tools. The project aims to generate content maps for HDF4 files containing valuable EOS data before the HDF4 library and tools are no longer maintained.
This document provides information about HDF (Hierarchical Data Format) tools and resources for working with Earth observation data. It summarizes HDF's focus on helping users at different stages of working with data, from initial product design to long-term archiving. It also describes specific HDF tools for viewing, comparing, converting between formats and adding metadata to scientific data files.
This document discusses the transition from HDF4 to HDF5 data formats. It notes that both formats will be used for many years and support is needed for both. It recommends using HDF5 for new projects but also supporting HDF4. The document outlines goals of supporting data and library interoperability, converting data and software between the formats, and getting HDF4 into a stable state to be revived when needed. It suggests converting data may be easier than writing readers for both formats but each case will be different.
The tool takes HDF-EOS 5 data as input, and generates COARDS-compatible output - if the input file has enough metadata to be COARDS-compliant, the output file will be COARDS-compliant. The tool is written in portable C, and ought to run on any platform where the HDF-EOS and netCDF libraries are available.
This year, we have made two major enhancements to the converter:
It now automatically detects whether its input is HDF-EOS2 or HDF-EOS5 format, and handles either one. The previous tool worked with HDF-EOS5 only.
Its netCDF output attempts to conform to the new CF conventions (a superset of the COARDS conventions). This is primarily an improvement in its translation of Swath datasets, which CF handles much better than COARDS.
This document summarizes a presentation about the current status and future directions of the Hierarchical Data Format (HDF) software. It provides updates on recent HDF5 releases, development efforts including new compression methods and ways to access HDF5 data, and outreach resources. It concludes by inviting the audience to share wishes for future HDF development.
The 2011 ACSI Survey Summary document provides results from NASA's 2011 survey of Earth science data users. Some key findings include:
- Customer satisfaction with NASA EOSDIS was steady at 77 for the fourth year in a row, meeting data users' needs.
- The most commonly used data format was HDF-EOS/HDF, though not all users found it easy to use.
- Comments from users provided both positive feedback and suggestions for improving areas like search functionality, order processing, and format preferences.
This document discusses user analysis of the HDFEOS.org website and plans for future improvements. It finds that the majority of the site's 100 daily users are "quiet", not posting on forums or other interactive elements. The main user types are locators, who search for examples or data; mergers, who combine or mosaic datasets; and converters, who change file formats. The document outlines recent updates focused on these user types, like adding Python examples for subsetting and calculating latitude and longitude. It proposes future work on artificial intelligence/machine learning uses of HDF files and examples for processing HDF data in the cloud.
This document summarizes MathWorks' work to modernize MATLAB's support for HDF5. Key points include:
1) MATLAB now supports HDF5 1.10.7 features like single-writer/multiple-reader access and virtual datasets through new and updated low-level functions.
2) Performance benchmarks show some improvements but also regressions compared to the previous HDF5 version, and work continues to optimize code and support future versions.
3) There are compatibility considerations for Linux filter plugins, but interim solutions are provided until MathWorks can ship a single HDF5 version.
The document discusses migrating from HDF5 1.6 to HDF5 1.8. It provides an overview of new features in HDF5 1.8, including a revised file format, improvements to group storage, new link types like external links, and enhanced error handling. The document recommends helping with the transition to HDF5 1.8 by discussing beneficial new features and awareness of compatibility issues when moving from 1.6 to 1.8.
This tutorial is designed for new HDF5 users. We will go over a brief history of HDF and HDF5 software, and will cover basic HDF5 Data Model objects and their properties; we will give an overview of the HDF5 Libraries and APIs, and discuss the HDF5 programming model. Simple C and Fortran examples, and Java tool HDFView will be used to illustrate HDF5 concepts.
In this talk we will discuss caching and buffering strategies in HDF5. The information presented will help developers write more efficient applications and avoid performance bottlenecks.
The document discusses recent and upcoming improvements to parallel HDF5 for improved I/O performance on HPC systems. Recent improvements include reducing file truncations, distributing metadata writes across processes, and improved selection matching. Upcoming work includes a high-level HPC API, funding for Exascale-focused enhancements, and future improvements like asynchronous I/O and auto-tuning to parallel file systems. Performance tips are also provided like passing MPI hints and using collective I/O.
The document discusses HDF and netCDF data support in ArcGIS. It provides an overview of how HDF and netCDF data can be directly ingested and used as raster datasets, mosaic datasets, feature layers, and tables in ArcGIS. This allows for scientific data to be displayed, analyzed, and shared using common GIS tools and services. It also describes existing Python tools for working with netCDF data and outlines future areas of development, including improved support for HDF5, THREDDS/OPeNDAP access, and evolving data standards.
The document describes H5Coro, a new C++ library for reading HDF5 files from cloud storage. H5Coro was created to optimize HDF5 reading for cloud environments by minimizing I/O operations through caching and efficient HTTP requests. Performance tests showed H5Coro was 77-132x faster than the previous HDF5 library at reading HDF5 data from Amazon S3 for NASA's SlideRule project. H5Coro supports common HDF5 elements but does not support writing or some complex HDF5 data types and messages to focus on optimized read-only performance for time series data stored sequentially in memory.
In this presentation, we will give an update on the HDF OPeNDAP project. We will update the new features inside the HDF5 OPeNDAP data handler. We will also introduce the enhanced HDF4 OPeNDAP data handler and demonstrate how it can help users to view and analyze remote HDF-EOS2 data. A demo that uses OPeNDAP client tools to handle AIRS and MODIS Grid/Swath data with the enhanced handler will be presented.
This document compares different methods for accessing HDF and netCDF files stored on Amazon S3, including Apache Drill, THREDDS Data Server (TDS), and HDF5 Virtual File Driver (VFD). A benchmark test of accessing a 24GB HDF5/netCDF-4 file on S3 from Amazon EC2 found that TDS performed the best, responding within 2 minutes, while Apache Drill failed after 7 minutes. The document concludes that TDS 5.0 is the clear winner based on performance and support for role-based access control and HDF4 files, but the best solution depends on use case and software.
The document describes the HDF Product Designer software tool. It was created to facilitate the design of interoperable scientific data products in HDF5 format. The tool allows intuitive editing of HDF5 objects and supports conventions like CF and ACDD. It also provides validation services to test file compliance. The goal is to help scientists design data products that follow standards and are easy for others to use.
HDF5 and Zarr are data formats that can be used to store and access scientific data. This presentation discusses approaches to translating between the two formats. It describes how HDF5 files were translated to the Zarr format by creating a separate Zarr store to hold HDF5 file chunks, and storing chunk location metadata. It also discusses an implementation that translates Zarr data to the HDF5 format by using a special chunking layout and storing chunk information in an HDF5 compound dataset. Limitations of the translations include lack of support for some HDF5 dataset properties in Zarr, and lack of support for some Zarr compression methods in the HDF5 implementation.
NetCDF and HDF5 are data formats and software libraries used for scientific data. NetCDF began in 1989 and allows for array-oriented data with dimensions, variables, and attributes. NetCDF-4 introduced new features while maintaining backward compatibility. It uses HDF5 for data storage and can read HDF4/HDF5 files. NetCDF provides APIs for C, Fortran, Java, and is widely used for earth science and climate data. It supports conventions, parallel I/O, and reading many data formats.
This document discusses three approaches to integrating HDF5 data with databases: I) Using PyTables to treat HDF5 like a database, II) Using HDF5 and a relational database side-by-side, and III) Fully integrating HDF5 and a relational database where the HDF5 data model is exposed to SQL and other database technologies.
A preponderance of data from NASA's Earth Observing System (EOS) is archived in the HDF Version 4 (HDF4) format. The long-term preservation of these data is critical for climate and other scientific studies going many decades into the future. HDF4 is very effective for working with the large and complex collection of EOS data products. Unfortunately, because of the complex internal byte layout of HDF4 files, future readability of HDF4 data depends on preserving a complex software library that can interpret that layout. Having a way to access HDF4 data independent of a library could improve its viability as an archive format, and consequently give confidence that HDF4 data will be readily accessible forever, even if the HDF4 library is gone.
To address the need to simplify long-term access to EOS data stored in HDF4, a collaborative project between The HDF Group and NASA Earth Science Data Centers is implementing an approach to accessing data in HDF4 files based on the use of independent maps that describe the data in HDF4 files and tools that can use these maps to recover data from those files. With this approach, relatively simple programs will be able to extract the data from an HDF4 file, bypassing the need for the HDF4 library.
A demonstration project has shown that this approach is feasible. This involved an assessment of NASA�s HDF4 data holdings, and development of a prototype XML-based layout mapping language and tools to read layout maps and read HDF4 files using layout maps. Future plans call for a second phase of the project, in which the mapping tools and XML schema are made production quality, the mapping schema are integrated with existing XML metadata files in several data centers, and outreach activities are carried out to encourage and facilitate acceptance of the technology.
The document discusses the HDF4 Mapping Project which aims to ensure long-term access to Earth Observing System (EOS) data stored in HDF4 files. It provides an overview of the project scope, including developing a proof of concept prototype and production quality mapping tools. It also describes verification studies conducted with NASA data centers to identify requirements for verifying correctness of HDF4 file content maps produced by the mapping tools. The project aims to generate content maps for HDF4 files containing valuable EOS data before the HDF4 library and tools are no longer maintained.
This document provides information about HDF (Hierarchical Data Format) tools and resources for working with Earth observation data. It summarizes HDF's focus on helping users at different stages of working with data, from initial product design to long-term archiving. It also describes specific HDF tools for viewing, comparing, converting between formats and adding metadata to scientific data files.
This document discusses the transition from HDF4 to HDF5 data formats. It notes that both formats will be used for many years and support is needed for both. It recommends using HDF5 for new projects but also supporting HDF4. The document outlines goals of supporting data and library interoperability, converting data and software between the formats, and getting HDF4 into a stable state to be revived when needed. It suggests converting data may be easier than writing readers for both formats but each case will be different.
Update on HDF, including recent changes to the software, upcoming releases, collaborations, future plans. Will include an overview of the upcoming HDF5 1.8 release, and updates on the netCDF4/HDF5 merge, HDF5 support for indexing, BioHDF, the HDF5-Storage Resource Broker project, the NPOESS BAA, HDF5-OPeNDAP project, HDF-EOS library and website supports and the HDF spin-off THG.
Update on HDF, including recent changes to the software, new releases, THG collaborations, and future plans. Session will include an overview of the HDF4.2r2, HDF5 1.6.6, and 1.8.0 releases, as well as updates on completed and on-going THG projects including crash-proofing HDF5, efficient append to HDF5 datasets, and indexing in HDF5.
Overview Of the HDF5 Lite and High Level interfaces.
Source: http://hdfeos.org/workshops/ws07/presentations/McGrath3/McGrath_HDF5_High_Level_and_Lite_Libraries_Intro.ppt
This document provides an overview of HDF5 (Hierarchical Data Format version 5) and introduces its core concepts. HDF5 is an open source file format and software library designed for storing and managing large amounts of numerical data. It supports a data model with objects such as datasets, groups, attributes, and datatypes. HDF5 files can be accessed through its software library and APIs from languages like C, Fortran, C++, Python and more. The document covers HDF5's data model, file format, programming interfaces, tools and example code.
Fast partial access to objects from very large files in the SDSC Storage Resource Broker (SRB5) can be extremely challenging, even when those objects are small. The HDF-SRB project integrates the SRB and NCSA Hierarchical Data Format (HDF5), to create an access mechanism within the SRB that is can be orders of magnitude more efficient than current methods for accessing object-based file formats.
The project provides interactive and efficient access to datasets or subsets of datasets in large files without bringing entire files into local machines. A new set of data structures and APIs have been implemented to the SRB support such object-level data access. A working prototype of the HDF5-SRB data system has been developed and tested. The SRB support is implemented in HDFView as a client application.
In this talk, we will give an update on the HDF5 OPeNDAP project. We will update the new features inside OPeNDAP HDF5 data handler. We will also introduce a new HDF5-Friendly OPeNDAP client library and demonstrate how it can help users to view and analyze remote HDF-EOS5 data served by OPeNDAP HDF5 handler. A demo will be presented with a customized OPeNDAP visualization client (GrADS) that uses the library.
The HDF Product Designer is an application for organizing HDF5 file content and metadata to support data standards conventions. It allows users to collaboratively design HDF5 file structure, then generate template files and code to write files in different programming languages. The tool originated from work on the ICESat-2 mission. Future work may include a web-based version, additional language support, and checks for standard compliance and interoperability. The goal is to help projects design HDF5 files earlier to reduce risks from late redesigns.
The document provides an update from The HDF Group on their activities related to Earth science data. The HDF Group maintains HDF software and provides services to users. They work on projects for NASA, NOAA, and other government agencies to help manage large Earth science data using HDF formats. Recent activities include support for EOSDIS, JPSS, and other missions through tool development, web services, and data standards work.
This 2009 tutorial slide will cover basic HDF5 Data Model objects and their properties. It will include an overview of the HDF5 Libraries and APIs, and describe the HDF5 programming model. Simple programming examples and the HDFView data browser will be used to illustrate HDF5 concepts and start developing your own HDF5 based applications.
This tutorial is for new HDF5 users.
An update on HDF, including a status report on the HDF Group, an overview of recent changes to the HDF4 and HDF5 libraries and tools, plans for future releases, HDF Group projects and collaborations, and future plans.
The document provides an overview and status update of the Earth Science Data and Information System (ESDIS). ESDIS has successfully supported numerous Earth science satellite missions and currently manages over 2 petabytes of science data. In fiscal year 2002, ESDIS delivered over 16 million data products to more than 1.8 million users. ESDIS is working to enhance its capabilities through initiatives like Data Pools and the EOS ClearingHOuse (ECHO) metadata broker. HDF-EOS 5 development is nearly complete and the workshop will discuss next steps for HDF-EOS tools and community adoption of HDF-EOS 5.
This tutorial is designed for new HDF5 users. We will cover HDF5 abstractions such as datasets, groups, attributes, and datatypes. Simple C examples will cover the programming model and basic features of the API, and will give new users the knowledge they need to navigate through the rich collection of HDF5 interfaces. Participants will be guided through an interactive demonstration of the fundamentals of HDF5.
This tutorial is for new HDF5 users.
The goal of this talk is to educate HDF5 users about backward and forward compatibility issues across releases of the HDF5 Library and versions of the HDF5 file format. We will discuss changes in the file format that were done to support new HDF5 features such as object creation order, compact groups, efficient access to the variable length data, UTF-8 encoding, external links, etc., and their implications on the HDF5 Library and users' applications.
The HDF Group creates and maintains HDF5, a file format and software library for managing large, complex datasets. Its goals are to enable effective management of data throughout its lifecycle and establish a sustainable organization to accomplish this. HDF5 is used widely in science and industry, with notable applications at NASA, NOAA, and national laboratories. It addresses challenges of complex data organization, efficient storage and access, and long-term data preservation.
The document discusses HDF for the cloud, including new features of the HDF Server and what's next. Key points:
- HDF Server uses a "sharded schema" that maps HDF5 objects to individual storage objects, allowing parallel access and updates without transferring entire files.
- Implementations include HSDS software that uses the sharded schema with an API and SDKs for different languages like h5pyd for Python.
- New features of HSDS 0.6 include support for POSIX, Azure, AWS Lambda, and role-based access control.
- Future work includes direct access to storage without a server intermediary for some use cases.
The HDF Group is in the process of updating HDF-EOS web site. During the workshop, we would like to share with audiences some useful information in the new website that can help users to have easy access of NASA HDF and HDF-EOS data.
The presentation includes three parts:
EOS User Forum: will introduce the EOS user forum and how users can benefit from this forum.
Tools: will present information on how to use several widely-used tools to access NASA HDF and HDF-EOS data.
Examples: will present several examples on how to use C, Fortran and IDL to access NASA HDF and HDF-EOS data.
HDF Cloud provides scalable HDF5 data access in the cloud. It uses a RESTful interface to store HDF5 files and metadata on object storage like AWS S3. This allows datasets to be accessed elastically from anywhere, avoiding hardware costs while gaining redundancy, scalability and other cloud benefits. The architecture maps HDF5 objects to individual storage objects, caching frequently used data in memory for high performance. Client libraries provide transparent access whether files are local or remote.
HDF Cloud Services aims to bring HDF5 to the cloud by defining a REST API for HDF5 and implementing related services. The HDF REST API allows HDF5 data to be accessed via HTTP requests and responses. H5serv is an open source reference implementation of the HDF REST API. The HDF Scalable Data Service (HSDS) is being developed to support large HDF5 repositories in a scalable, cost effective manner using object storage like AWS S3.
This document discusses how to optimize HDF5 files for efficient access in cloud object stores. Key optimizations include using large dataset chunk sizes of 1-4 MiB, consolidating internal file metadata, and minimizing variable-length datatypes. The document recommends creating files with paged aggregation and storing file content information in the user block to enable fast discovery of file contents when stored in object stores.
This document provides an overview of HSDS (Highly Scalable Data Service), which is a REST-based service that allows accessing HDF5 data stored in the cloud. It discusses how HSDS maps HDF5 objects like datasets and groups to individual cloud storage objects to optimize performance. The document also describes how HSDS was used to improve access performance for NASA ICESat-2 HDF5 data on AWS S3 by hyper-chunking datasets into larger chunks spanning multiple original HDF5 chunks. Benchmark results showed that accessing the data through HSDS provided over 2x faster performance than other methods like ROS3 or S3FS that directly access the cloud storage.
This document summarizes the current status and focus of the HDF Group. It discusses that the HDF Group is located in Champaign, IL and is a non-profit organization focused on developing and maintaining HDF software and data formats. It provides an overview of recent HDF5, HDF4 and HDFView releases and notes areas of focus for software quality improvements, increased transparency, strengthening the community, and modernizing HDF products. It invites support and participation in upcoming user group meetings.
This document provides an overview of HSDS (HDF Server and Data Service), which allows HDF5 files to be stored and accessed from the cloud. Key points include:
- HSDS maps HDF5 objects like datasets and groups to individual cloud storage objects for scalability and parallelism.
- Features include streaming support, fancy indexing for complex queries, and caching for improved performance.
- HSDS can be deployed on Docker, Kubernetes, or AWS Lambda depending on needs.
- Case studies show HSDS is used by organizations like NREL and NSF to make petabytes of scientific data publicly accessible in the cloud.
This document discusses creating cloud-optimized HDF5 files by rearranging internal structures for more efficient data access in cloud object stores. It describes cloud-native and cloud-optimized storage formats, with the latter involving storing the entire HDF5 file as a single object. The benefits of cloud-optimized HDF5 include fast scanning and using the HDF5 library. Key aspects covered include using optimal chunk sizes, compression, and minimizing variable-length datatypes.
This document discusses updates and performance improvements to the HDF5 OPeNDAP data handler. It provides a history of the handler since 2001 and describes recent updates including supporting DAP4, new data types, and NetCDF data models. A performance study showed that passing compressed HDF5 data through the handler without decompressing/recompressing led to speedups of around 17-30x by leveraging HDF5 direct I/O APIs. This allows outputting HDF5 files as NetCDF files much faster through the handler.
This document provides instructions for using the Hyrax software to serve scientific data files stored on Amazon S3 using the OPeNDAP data access protocol. It describes how to generate ancillary metadata files called DMR++ files using the get_dmrpp tool that provide information about the data file structure and locations. The document explains how to run get_dmrpp inside a Docker container to process data files on S3 and generate customized DMR++ files that the Hyrax server can use to serve the files to clients.
This document provides an overview and examples of accessing cloud data and services using the Earthdata Login (EDL), Pydap, and MATLAB. It discusses some common problems users encounter, such as being unable to access HDF5 data on AWS S3 using MATLAB or read data from OPeNDAP servers using Pydap. Solutions presented include using EDL to get temporary AWS tokens for S3 access in MATLAB and providing code examples on the HDFEOS website to help users access S3 data and OPeNDAP services. The document also notes some limitations, such as tokens being valid for only 1 hour, and workarounds like requesting new tokens or using the MATLAB HDF5 API instead of the netCDF API.
The HDF5 Roadmap and New Features document outlines upcoming changes and improvements to the HDF5 library. Key points include:
- HDF5 1.13.x releases will include new features like selection I/O, the Onion VFD for versioned files, improved VFD SWMR for single-writer multiple-reader access, and subfiling for parallel I/O.
- The Virtual Object Layer allows customizing HDF5 object storage and introduces terminal and pass-through connectors.
- The Onion VFD stores versions of HDF5 files in a separate onion file for versioned access.
- VFD SWMR improves on legacy SWMR by implementing single-writer multiple-reader capabilities
HSDS provides HDF as a service through a REST API that can scale across nodes. New releases will enable serverless operation using AWS Lambda or direct client access without a server. This allows HDF data to be accessed remotely without managing servers. HSDS stores each HDF object separately, making it compatible with cloud object storage. Performance on AWS Lambda is slower than a dedicated server but has no management overhead. Direct client access has better performance but limits collaboration between clients.
This document discusses STARE-PODS, a proposal to NASA/ACCESS-19 to develop a scalable data store for earth science data using the SpatioTemporal Adaptive Resolution Encoding (STARE) indexing scheme. STARE allows diverse earth science data to be unified and indexed, enabling the data to be partitioned and stored in a Parallel Optimized Data Store (PODS) for efficient analysis. The HDF Virtual Object Layer and Virtual Data Set technologies can then provide interfaces to access the data in STARE-PODS in a familiar way. The goal is for STARE-PODS to organize diverse data for alignment and parallel/distributed storage and processing to enable integrative analysis at scale.
This document summarizes new features in HDF5 1.12.0, including support for storing references to objects and attributes across files, new storage backends using a virtual object layer (VOL), and virtual file drivers (VFDs) for Amazon S3 and HDFS. It outlines the HDF5 roadmap for 2019-2022, which includes continued support for HDF5 1.8 and 1.10, and new features in future 1.12.x releases like querying, indexing, and provenance tracking.
The document discusses leveraging cloud resources like Amazon Web Services to improve software testing for the HDF group. Currently HDF software is tested on various in-house systems, but moving more testing to the cloud could provide better coverage of operating systems and distributions at a lower cost. AWS spot instances are being used to run HDF5 build and regression tests across different Linux distributions in around 30 minutes for approximately $0.02 per hour.
Google Colaboratory allows users to write and execute Python code in the cloud using Jupyter notebooks. It provides a free GPU and TPU for accelerating code. The document discusses how HDF-EOS is a standard format for satellite data and provides many examples for converting and processing HDF-EOS data. It then demonstrates how to install necessary packages and run an example zoo code in Colab to plot HDF-EOS data in the cloud without installing anything locally.
The document discusses Parallel Computing with HDF Server. The key points are:
1. HDF Server (HSDS) allows efficient access to HDF5 data stored in AWS S3. It runs as containers on Kubernetes and supports parallel access across containers.
2. HSDS uses S3 as the data store for HDF5 files. Individual HDF5 objects like datasets and chunks are stored as separate S3 objects. This allows parallel read/write and only modifying what changes.
3. HDF Kita Lab is a hosted Jupyter environment on AWS that provides access to HSDS for reading and writing HDF5 data on S3. It allows scaling the server and provides tools for HDF5 on S3.
This document summarizes a presentation on best practices for creating Earth observation data products that follow the Hierarchical Data Format for Earth Observing System (HDF-EOS) standard. It provides guidance on including geo-location variables, using named dimensions, following Climate and Forecast Metadata conventions, testing products with various tools, and using the HDF Product Designer tool to help design and validate compliant products. The work aims to improve the usability of data products and reduce questions received through help desks.
This document provides an update on HDF (Hierarchical Data Format) releases and features. It discusses the current HDF5 1.10 series which allows controlling HDF5 file versioning and enables compression for parallel writes. The upcoming HDF5 1.12 release will include non-POSIX I/O using a Virtual Object Layer, UTF-8 encoding, and other file format changes. The document shows examples of compressing Sentinel and SeaSat data files in HDF5 using different compression methods.
The Terra Data Fusion Project aims to fuse data from NASA's Terra satellite's five instruments - ASTER, CERES, MISR, MODIS, and MOPITT - into a single product. This presents challenges due to the huge data volumes, different locations of input data, instrument granularities, data storage methods, and file formats. The project overcame these by using NCSA supercomputing facilities, converting files to HDF5, organizing data by instrument and granule, and adding metadata. The final fused data product contains over 1 million input files totaling 2.3 petabytes, reduced to 1 petabyte using HDF5 compression.
This document discusses strategies for storing and accessing HDF5 data files in cloud object storage like Amazon S3. It describes an HDF5 Virtual File Driver (VFD) developed by The HDF Group that allows reading HDF5 files directly from S3 without downloading. For better performance, the document recommends optimizing HDF5 files stored in S3 by using chunking, compression, and aggregating smaller files. It also introduces the HDF Cloud Schema which maps HDF5 objects to individual object storage objects for parallel access.
The document discusses an S3 VFD that allows HDF5 files to be served from object storage. It uses existing HDF5 libraries and new VFD drivers to access HDF5 files stored in S3. The S3 VFD uses range gets to read desired data and optimization is performed to avoid small metadata accesses. A new API and data structure are introduced to set credentials for the S3 VFD when opening HDF5 files.
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HDF Update
1. HDF Update
Mike Folk
National Center for Supercomputing Applications
HDF and HDF-EOS Workshop VI
December 4-5, 2002
-1-
HDF
2. Topics
• Who is supporting HDF
• HDF software in 2002
• Other activities of interest
-2-
HDF
3. Who is supporting HDF?
• NASA/ESDIS
– Earth science applications, instrument data
• DOE/ASCI (Accelerated Strategic Computing Init.)
– Simulations on massively parallel machines
• NCSA/NSF/State of Illinois
– HPC and Grid data intensive apps, Visualization, user support
– Atmospheric and ocean modeling environments
• DOE Scientific Data Analysis & Computation Program
– High performance I/O R & D
• National Archives and Records Administration
– Small grant to consider HDF5 as an archive format
-3-
HDF
4. HDF software in 2002
•
•
•
•
•
Library releases
Java Products
Tools
Compression
Investigations of Web technologies
-4-
HDF
5. HDF4 library
• No releases in 2002.
• Release 1.6 planned for May, 2003
– Bug fixes
– New compilers
• Intel
• Portland Group
– New OS
• Mac OS X
• AIX 5.1 64-bit
-5-
HDF
6. HDF5 software milestones in 2002
Q1 ‘02
Base
library
High level
library
Java
products
Other
tools
Q2 ‘02
Q3 ‘02
3
.4.
1
F5
HD
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ta
0
a
av ts 1.
J c
du
pro
5
-H sion
H4 er
onv y
c ar
- 6 - libr
.4.
1
Q4 ‘02
4
5
.4.
1
e
ev
hl
Hig Is
AP
a
va 1.1
a ts
av .2
J c
J 1
du
ds
pro
pro
ort
p
im
H5
HDF
7. HDF5 library in 2002
• Compilers, configuration, etc.
– “h5cc” script to simplify compilation of HDF5
programs
– F90 shared library and C++ supported on Windows
– Intel C, F90 and C++ on Linux, IA32/64 and Windows
– Support for zlib 1.1.4
• Performance
– Added library performance tests
– Performance improvements
• hyperslabs, data conversions. chunking
– Fewer and larger I/O requests when accessing a file
– Parallel I/O performance improvements
-7-
HDF
8. Parallel HDF5
• Parallel I/O performance benchmark suite
– Compares raw I/O, MPI-I/O, and HDF5 I/O
– Distributed with HDF5
– http://hdf/RFC/PIO_Perf/PHDF5_performance.html
• Parallel HDF5 tutorial
– http://hdf.ncsa.uiuc.edu/HDF5/doc/Tutor/
• “Flexible parallel HDF5” programming model
– More flexible model for parallel HDF5
• Performance studies and tuning activities
-8-
HDF
9. Next major release -- HDF5 1.6
• Release date: Spring 2003
• New format and library features include
–
–
–
–
Compression enhancements, including szip
Generic Properties
Checksum
Dimension scale support (tentative)
• Performance improvements include
– Chunking & compression
– Parallel I/O performance benchmark suite
-9-
HDF
10. Next major release -- HDF5 1.6
• Flexible parallel HDF5
• Special platforms
–
–
–
–
–
Large Compaq cluster (Pittsburgh SC)
Crays
Windows XP
Mac
Several new compilers (e.g. Intel, Portland Group)
• Documentation
– New User’s Guide-good draft, first version
- 10 -
HDF
11. High level APIs
• Make HDF5 easier to use
– More operations per call than the normal HDF5 API
• Encourage standard ways to store objects
– Enforce standard representation of objects in HDF5
- 11 -
HDF
12. High level APIs
• Lite – done
– Same as HDF5, but simpler
• Image – done
– Interprets dataset as image/palette
– 2-D raster data like HDF4 raster images
• Table – partly done
– Interprets dataset as “tables” – collections of records
– Insert, delete records or fields
– Future: sort and search
• Dimension scale – in the works
• Unstructured grids – in the works
• http://hdf.ncsa.uiuc.edu/HDF5/hdf5_hl/doc/
- 12 -
HDF
14. HDF Java Products – 2002
• Goal: replace older tools with single viewer/editor
• HDF Java Products
–
–
–
–
Java HDF Interface (JHI) – to access the HDF4 library.
Java HDF5 Interface (JHI5) – to access the HDF5 library.
New hdf-object package – understands HDF4 and HDF5.
HDFView – tool for browsing/editing HDF4 and HDF5
• See demo, brochure, CD, web page
– http://hdf.ncsa.uiuc.edu/hdf-java-html/
- 14 -
HDF
15. HDFView releases in 2002
Q2
Version 1.0
Browser for
both HDF4
and HDF5
Q3
Version 1.1
Editor for
both HDF4
and HDF5
Q4
Version 1.2
All features of
old Java tools.
Some new
features.
HDFView can do as much as JHV and H5View and
also includes many new editing features
http://hdf.ncsa.uiuc.edu/hdf-java-html/hdfview/
- 15 -
HDF
16. H4toH5 Conversion Toolkit
• Goal: support transition from HDF4 to HDF5
• Version 1.0 released in July 2002
• Includes
– h4toh5 converter
– h5toh4 converter
– library of functions for converting HDF4 objects into
HDF5 objects
• Download from:
– http://hdf.ncsa.uiuc.edu/h4toh5/libh4toh5.html
• Mapping specification and FAQ
– http://hdf.ncsa.uiuc.edu/HDF5/doc/ADGuide/H4toH5Mapping.pdf
- 16 -
HDF
17. Other tools work
• H5import - convert flat files to HDF5 datasets
– ASCII text file with numeric data (float or integer)
– Binary file with native floating point data
– Binary file with native integer data
• hdf4import – souped up version of the old fptohdf
– Available in hdf4r1.6
• HDF5-to-GIF and GIF-to-HDF5 converters
• H5dump improvements
– Subsetting
– Support variable length datatypes including strings
- 17 -
HDF
18. Other tools work
• H5diff
– compare the structure and contents of two HDF5 files,
and report differences
– Command line utility like Unix ‘diff’ and older ‘hdiff’
– Report missing objects, inconsistent size, datatype, etc.
– Compare values of numeric datasets
– First beta available January 2003
– RFC: http://hdf.ncsa.uiuc.edu/RFC/H5diff/h5diff.html
- 18 -
HDF
19. Compression
• Szip - fast compression method for EOS data
– Expect to include in next releases of HDF4 and HDF5
• Shuffling – reorder bytes before compressing
– Can improve compression ratio
• Performance study – BZIP2 vs gzip compression
– Study: whether or not to support bzip2 compression
– Result: BZIP2 not significantly better than gzip
– So not currently supported in the release
– But BZIP2 can be used with HDF5
- 19 -
HDF
21. HDF5 XML
• Great interest in XML, interoperation of XML and
binary formats
• Results
– HDF5 DTD
– h5dump –XML
– H5View reads XML and writes HDF5
• Studies, design notes, other info
– http://hdf.ncsa.uiuc.edu/HDF5/XML/
• Possible future activity:
–
–
–
–
XML schema
Update tools
HDF4 schema, tools
Format translation via XSLT
- 21 -
HDF
22. XML, Java Server Pages, etc.
• How to use HDF5 data in Web environment
• Experiments with XML, Java Server Pages
(JSP), etc.
– JSP server
• Access HDF5 files on Web server using Web browser,
or Java applet, or Java application
– Several variations demonstrated
– Is not a product!
• http://hdf.ncsa.uiuc.edu/HDF5/XML/
- 22 -
HDF
23. CORBA Experiments
• HDF5 with CORBA on distributed systems
– Prototype CORBA server to wrap HDF5 library
and datasets (C++)
– Remote access via C++, Java, Web
– Might be valuable as replacement for Java Native
Interface
– Successful demonstration, but many open issues
– Is not a product!
http://hdf.ncsa.uiuc.edu/HDF5/XML/JSPExperiments/index.html
- 23 -
HDF
25. NPOESS
• National Polar-orbiting Operational Environmental
Satellite System
– Combine satellite systems of civil and defense programs
• HDF5 to be used to distribute data to users
• First implementation in 2006
– Support the NPOESS Preparatory Program
• Later full implementation by 2013
– Converged system provides global coverage
• http://www.ipo.noaa.gov
- 25 -
HDF
26. Neutron Research Community
• Worldwide research community
– England, France, Germany, Japan, Italy, Switzerland, Russia
– US centers at Argonne, NIST, Los Alamos
• Neutron and X-ray scattering experiments and simulations
– Common software and formats to gather, share, archive, postprocess data
• NeXus data format
–
–
–
–
Enforces standardization of metadata and data structures
Based on HDF4 for many years
Now switching to HDF5
http://www.neutron.anl.gov/nexus/
- 26 -
HDF
27. National Archives and
Records Administration
• Pilot project for HDF5
• Explore scientific data format requirements
for long term archiving of electronic records
• Identify record types for which HDF5 is
suited
- 27 -
HDF
28. Atmospheric and Ocean Models
•
•
Modeling Environment for Atmospheric
Discovery (MEAD)
HDF5 for high performance I/O for
atmospheric and ocean modeling
– Weather Research and Forecasting (WRF) model
– Regional Ocean Modeling System (ROMS)
– Coupling of WRF and ROMS
•
UAH ESML & data mining also involved
- 28 -
HDF
29. HDF5 Mesh API prototype
• Support for structured and unstructured “mesh” data
• For applications such as computational fluid
dynamics, finite element analysis, and visualization.
• A higher-level API
• Format
– HDF5 groups and datasets to organize the data
• Collaboration involving NCSA, CEI and others
• Documentation still pretty sketchy, but see
• ftp://ftp.ensight.com/pub/HDF_RW/hdf_rw.tgz
• Discussion list in the works
- 29 -
HDF
30. HDF5 Wins 2002 R&D Magazine Award
“The 100 products and processes that are the
most ‘technologically significant’ and can
change people's lives for the better”
http://www.ncsa.uiuc.edu/News/Access/Releases/020722.HDF5.html
- 30 -
HDF
31. Thank you!
Information Sources
HDF • HDF website
– http://hdf.ncsa.uiuc.edu/
5 • HDF5 Information Center
– http://hdf.ncsa.uiuc.edu/HDF5/
• HDF Helpdesk
– hdfhelp@ncsa.uiuc.edu
• HDF users mailing list
– hdfnews@ncsa.uiuc.edu
- 31 -
HDF
33. HDF5 funding sources
Other
DOE 4%
SciDAC
4%
State of IL
10%
NASA
37%
NSF
14%
ASCI
31%
NASA
ASCI
NSF
State of IL DOE SciD Other
$588,000 $495,000 $225,553 $162,750 $70,000 $60,000
- 33 -
HDF
34. HDF5 User Community
• Worldwide use in government, academia, industry
• How many users?
– 450 organizations or individuals have filled in “user” form in the past
year
– There are many times this many anonymous users
– And some organizations have thousands of users (e.g. the Earth
Observing System)
• Public applications
– More than 25 publicly available applications
– Four vendors so far
•
•
•
•
•
LabVIEW
IDL
EarthScan Network
HDF Explorer
Others in the works (e.g. Matlab)
- 34 -
HDF
35. Technical fields that use HDF5
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
Aerospace
Agricultural research
Air traffic control
Aircraft emissions database
Applied mathematics
Astrophysics
Astrophysics / supernovae
Atmospheric chemistry
Atmospheric physics
Bioengineering
CEM Simulation
Climatology / hydrology
Computational fluid dynamics
Computational physics
Computational physics /
education
Computational physics and
computational astrophysics
Computer modeling
Computer science
Data processing
Earth observation /
atmospheric science
Earth science
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
Environmental science
Fast searching, sorting and retrieval
Film making special effects
Fluid mechanics
GIS
Geodetic Science
Geology
Gravitational physics
Hydrology
Information technology
Magnetic mass spectrometer
development
Marine biology / ecology
Materials science
Meteorological data products
Meteorology
Microscopy
Molecular biology
Nano device simulation
Neutron scattering
Ocean color
Ocean remote sensing
Optics / optoelectronics
Petroleum engineering
- 35 -
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
Photonic band gap studies
Photonic crystals
Photonics
Post-fire erosion analysis
Protein crystallography,
molecular modeling
Protostellar accretion discs
Remote sensing
SAR processing
Satellite / weather radar remote
sensing
Satellite oceanography
Semiconductor process
simulation
Software engineering, distributed
systems
Space geodesy
Space physics
Surface water flow and sediment
transport
Theoretical chemistry
Visualization
Volcanology
Water resources management
X-ray physics
HDF
38. Next major release -- HDF5 1.6
• Performance improvements
–
–
–
–
–
Chunking
Compression (several)
Parallel I/O
Metadata I/O
Compact dataset storage
• Other parallel
– Parallel I/O performance benchmark suite
– Flexible parallel HDF5
– Portland group C, Fortran 90 and C++ compilers
– Quite a bit of Fortran work
- 38 -
HDF
39. Next major release -- HDF5 1.6
• Testing (several)
• Special platforms
–
–
–
–
–
PSC cluster
Cray
Windows XP
Mac
Several new compilers (e.g. Intel, Portland Group)
• Documentation
– New User’s Guide-good draft, first version
- 39 -
HDF
40. HDF5 High Level APIs – HDF5 Image
• For datasets to be interpreted as images/palettes
– 2-D raster data like HDF4 raster images
• Image operations
– Create, write, read, query
• Based on “HDF5 Image & Palette Specification”
- 40 -
HDF
41. HDF5 High Level APIs – HDF5 Table
• For datasets to be interpreted as “tables”
– A collection of records
– All records have the same structure
– Like Vdatas in HDF4, but more operations
• Table operations
–
–
–
–
Create, write, read, query
Insert, delete records or fields
Future: sort and search
Includes the following new Table functions:
- 41 -
HDF
42. HDF5 High Level APIs – HDF5 Table
• For datasets to be interpreted as “tables”
– A collection of records
– All records have the same structure
– Like Vdatas in HDF4, but more operations
• Table operations
– Create, write, read, query
– Insert, delete records or fields
– Later: sort and search
- 42 -
HDF
43. HDF5 High Level API – Future
• Dimension scales
– Similar to HDF4
– In progress
• More table operations
– sort and search
• Unstructured grids
– E.g. triangle mesh
- 43 -
HDF
44. Szip Compression Software
• Implements CCSDS lossless compression algorithm
• Fast compression method for EOS data
• Expect to include in next releases of HDF4 and HDF5
– HDF4: compress SDS and image
– HDF5: compress datasets
• Intellectual property issues
–
–
–
–
Owned by U of Idaho (formerly U of New Mexico)
Open source
No commercial of encoder use without license
Decoder free for everyone
- 44 -
HDF
45. Performance study – BZIP2 compression
• Goal: decide whether or not to support bzip2 compression
• Compared bzip2 and gzip
• Observations
– Bzip2 always better than gzip in compression ratio
– But the difference was just a few percentage points
– And bzip2 always takes more processing time, especially for
decoding
• Result
– Not currently supported in the release
– But BZIP2 can be used with HDF5 (checked with HDF5-1.4.4)
• http://hdf.ncsa.uiuc.edu/HDF5/papers/bzip2/
- 45 -
HDF
46. New HDFView features
•
•
•
•
•
•
•
•
•
•
Display palette in graph as
separate RGB lines.
Open file as read-only option
Create new array from old array
Import data from text file
Save to HDF4, HDF5 or binary
Create new image from subset
of existing image
Modify string-type dataset
content
Convert jpeg to HDF image
Convert HDF to jpeg image
More user options and well
organized GUI
- 46 -
•
•
•
•
•
•
•
Select vdata or compound datatype
by field
Select subset from preview image
and using mouse
Support unlimited dimension when
creating new HDF4 dataset.
Enable application of simple math
calculations to data
Support multiple palettes/image
Create new image with default
attributes
Modify image palette or select
predefined palette
HDF
47. CORBA, XML etc. permutations
Java
HTML
Java C
Java
Java
Server
Native
Platform Interface
Web
browser
HDF
Library
and File
C
XML
Any
CORBA
Server
Applet
C++
Java
Native
Interface
C
Java
Java
Other
App.
Other
App.
Any
H5view,
etc
Java
Any
Client/Remote
Server/Local
Distributed Product
Demonstrated in Research
Should work, but not demonstrated
- 47 -
HDF
48. National Polar-orbiting Operational Environmental
Satellite System (NPOESS)
U.S. civil and defense programs to combine weather data collection, expanding to
global coverage and long-term continuity of observations at lessMETOP
cost!
POES
METOP
DMSP
0730
1330
0830
0530
0830
0530
1330
DMSP
Today
• 4-Orbit System
– 2 US Military
– 2 US Civilian
NPOESS
0530
0930
0930
1330
POES
POES
Local Equatorial
Crossing Time
NPOESS
DMSP
Local Equatorial
Crossing Time
DMSP
Tomorrow (2005)
2 US Military
1 US Civilian
1 EUMETSAT/METOP
- 48 - Distribute
Local Equatorial
Crossing Time
NPOESS
Lite
Future (2013)
2 US Converged
1 US “Lite”
1 EUMETSAT/METOP
Specialized Satellites
in HDF5 HDF
Editor's Notes
<number>
Use this as backup
backup
backup
backup
Backup slides
Backup slides
backup
Backup: Animation of all the permutations
NPOESS is evolving the United States’ 4 spacecraft polar-orbiting satellite system into a two satellite system based on U.S. civil and national security requirements. Consistent with the PDD, the NPOESS program is implementing the converged system in a manner that encourages cooperation with foreign governments and international organizations, specifically leveraging European developed payloads and relying on EUMETSAT to provide the satellite for the third plane of the 3-satellite Joint Polar System constellation that will ensure global coverage for key environmental data.
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