IDL Support of HDF
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This document discusses IDL's support for the HDF file format. It provides a brief history of IDL, noting its origins at NCAR and NASA and long history supporting data analysis. It describes IDL's HDF support, which was introduced in version 4.0 and expanded in version 5.6 to include HDF5. The document outlines key HDF file reading and writing capabilities in IDL and new features added in version 6.3 like opaque, variable-length, enumerated, and compound data support. It positions IDL as a powerful tool for data visualization, processing, and analysis across platforms.
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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.
Parallel HDF5 Introductory Tutorial
This Tutorial is designed for the users who have exposure to MPI I/O and basic concepts of HDF5 and would like to learn about Parallel HDF5 Library. The Tutorial will cover Parallel HDF5 design and programming model. Several C and Fortran examples will be used to illustrate the basic ideas of the Parallel HDF5 programming model. Some performance issues including collective chunked I/O will be discussed. Participants will work with the Tutorial examples and exercises during the hands-on sessions.
HDF Update
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.
Parallel Computing with HDF Server
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.
HDF5 iRODS
Numerous scientific teams use the HDF5 format to store very large datasets. Efficient use of this data in a distributed environment depends on client applications being able to read any subset of the data without transferring the entire file to the local machine. The goal of the HDF5-iRODS Project was to develop an HDF5-iRODS module for the iRODS datagrid server that supported this capability, and to apply the technology to an NCSA/SDSC Strategic Applications Program (SAP) project, FLASH.
A joint team from The HDF Group (representing NCSA) and the SDSC SRB group collaborated to accomplish the project goal. The team implemented five HDF5 microservices functions on the iRODS server, and developed an iRODS FLASH slice client application. The client implementation also includes a JNI interface that allows HDFView, a standard tool for browsing HDF5 files, to access HDF5 files stored remotely in iRODS. Finally, three new collection client/server calls were added to the iRODS APIs, making it easier for users to query the content of an iRODS collection.
HDF Update
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.
Introduction to HDF5 Data Model, Programming Model and Library APIs
Introduction to HDF5 Data Model, Programming Model and Library APIsThe HDF-EOS Tools and Information Center
This document provides an overview and introduction to the HDF5 data model, programming model, and library APIs. The morning session will include a lecture on HDF5 introduction, programming model, and library APIs. The afternoon will have hands-on sessions on introduction to HDF5 files, groups, datasets and attributes as well as advanced topics like hyperslab selections, compound datatypes, and parallel HDF5. The goals are to introduce HDF5, provide knowledge on how data is organized and used by applications, and provide examples of reading and writing HDF5 files. The afternoon aims to help users start working with the HDF5 library on the NCSA machines by running examples and creating their own programs.Similar to IDL Support of HDF (20)
Introduction to HDF5 Data Model, Programming Model and Library APIs
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HDF5 Advanced Topics - Object's Properties, Storage Methods, Filters, Datatypes
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Cloud-Optimized HDF5 Files
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.
Accessing HDF5 data in the cloud with HSDS
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.
The State of HDF
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.
Highly Scalable Data Service (HSDS) Performance Features
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.
Creating Cloud-Optimized HDF5 Files
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.
HDF5 OPeNDAP Handler Updates, and Performance Discussion
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.
Hyrax: Serving Data from S3
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.
Accessing Cloud Data and Services Using EDL, Pydap, MATLAB
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.
HDF - Current status and Future Directions
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
HDFEOS.org User Analsys, Updates, and Future
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.
HDF - Current status and Future Directions
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.
H5Coro: The Cloud-Optimized Read-Only Library
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.
MATLAB Modernization on HDF5 1.10
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.
HDF for the Cloud - Serverless HDF
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.
HDF5 <-> Zarr
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.
HDF for the Cloud - New HDF Server Features
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.
Apache Drill and Unidata THREDDS Data Server for NASA HDF-EOS on S3
Apache Drill and Unidata THREDDS Data Server for NASA HDF-EOS on S3The HDF-EOS Tools and Information Center
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.STARE-PODS: A Versatile Data Store Leveraging the HDF Virtual Object Layer fo...
STARE-PODS: A Versatile Data Store Leveraging the HDF Virtual Object Layer fo...The HDF-EOS Tools and Information Center
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.HDF5 Roadmap 2019-2020
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.
Leveraging the Cloud for HDF Software Testing
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.
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Apache Drill and Unidata THREDDS Data Server for NASA HDF-EOS on S3
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IDL Support of HDF
- 1. IDL Support of HDF Tad Scheiblich RSI December 2, 2005
- 2. What is IDL? Interactive Data Language • Extensive Cross-Platform Libraries: – Interactive Visualization – Data Processing – Scientific File Formats – GUI Development
- 3. History & Background • 28 year history with NCAR & NASA • Extensively used at NOAA • Based in Boulder, CO – Offices Worldwide • Currently on version 6.2
- 4. History with HDF • HDF Support Introduced in IDL 4.0 • HDF5 Support Added in IDL 5.6
- 5. The HDF Libraries ; create HDF5 file file = 'hdf5_out.h5' fid = H5F_CREATE(file) • Thin-Wrappers to Native C Libraries • HDF5 1.6.3 • HDF 4.1r5 ; create some data data = hanning(100,200) ; create a datatype datatype_id = H5T_IDL_CREATE(data) ; create a dataspace, allow the dataspace to be extendable dataspace_id = $ H5S_CREATE_SIMPLE([100,100],max_dimensions=[200,200]) ; create the dataset dataset_id = $ H5D_CREATE(fid,'Hanning',datatype_id,dataspace_id, $ chunk_dimensions=[20,20]) ; extend the size of the dataset to fit the data H5D_EXTEND,dataset_id,size(data,/dimensions) ; write the data to the dataset H5D_WRITE,dataset_id,data
- 6. Why IDL? • Interpreted language – easier to use – Interactive – Still very performant • Incredible visualization capabilities • Vast data processing library
- 7. HDF5 Enhancements in IDL 6.3 • Opaque Data Support • Variable Length Data Support • Enumerated Data Support • Compound Data Support
- 8. Other Features of Interest • • • • Virtual Machine iTools Interactive Data Analysis netCDF Support Cluster Computing API – MPI – Task-Farming • Connectivity Bridges – C/C++, Fortran (Dynamically Linked Modules) – Java – COM/ActiveX
- 9. For More Information http://www.RSInc.com/IDL (303) 786-9900 Thank You! -Tad Scheiblich
- 10. For More Information http://www.RSInc.com/IDL (303) 786-9900 Thank You! -Tad Scheiblich