Technical Insights

Christian Hoene, Symonics GmbH; and Piotr Majdak, Acoustics Research Institute; HDF Guest Bloggers Spatial audio - 3D sound.  Back in the ‘70’s, “dummy head” microphones were used to create spatial audio recordings. With headphones, one was able to listen to those recordings and marvel at the impressive spatial distribution of sounds – just like in real life. [caption id="attachment_11132" align="aligncenter" width="624"] Displays the difference between listening to a real source and listening to realistic virtual sounds via headphones[/caption] Nowadays, we have a much better understanding of the human binaural perception and we can even simulate spatial audio signals with the help of computers.  Indeed, a modern virtual reality (VR) headset such as the Oculus Rift or Samsung Gear utilizes 3D audio to allow...

The HDF Server allows producers of complex datasets to share their results with a wide audience base. We used it to develop the Global Fire Emissions Database (GFED) Analysis Tool, a website which guides the user through our dataset. A simple webmap interface allows users to select an area of interest and produce data visualization charts. ...

Mark Miller, Lawrence Livermore National Laboratory, Guest Blogger The HDF5 library has supported the I/O requirements of HPC codes at Lawrence Livermore National Labs (LLNL) since the late 90’s. In particular, HDF5 used in the Multiple Independent File (MIF) parallel I/O paradigm has supported LLNL code’s scalable I/O requirements and has recently been gainfully used at scales as large as 1,000,000 parallel tasks. What is the MIF Parallel I/O Paradigm? In the MIF paradigm, a computational object (an array, a mesh, etc.) is decomposed into pieces and distributed, perhaps unevenly, over parallel tasks. For I/O, the tasks are organized into groups and each group writes one file using round-robin exclusive access for the tasks in the group. Writes within groups are serialized but...

DOE has continued to partner with The HDF Group, supporting development of HDF5 through two generations of computing; sponsoring this development has benefited the entire HDF5 user community. Today, DOE supports current HDF5 R&D to ensure that the data challenges of third generation exascale computing ...

MuQun (Kent) Yang, The HDF Group

Many NASA HDF and HDF5 data products can be visualized via the Hyrax OPeNDAP server through Hyrax’s HDF4 and HDF5 handlers.  Now we’ve enhanced the HDF5 OPeNDAP handler so that SMAP level 1, level 3 and level 4 products can be displayed properly using popular visualization tools.

Organizations in both the public and private sectors use HDF to meet long term, mission-critical data management needs. For example, NASA’s Earth Observing System, the primary data repository for understanding global climate change, uses HDF.  Over the lifetime of the project, which began in 1999, NASA has stored 15 petabytes of satellite data in HDF which will be accessible by NASA data centers and NASA HDF end users for many years to come.

In a previous blog, we discussed the concept of using the Hyrax OPeNDAP web server to serve NASA HDF4 and HDF5 products.  Each year, The HDF Group has enhanced the HDF4 and HDF5 handlers that work within the Hyrax OPeNDAP framework to support all sorts of NASA HDF data products, making them interoperable with popular Earth Science tools such as NASA’s Panoply and UCAR’s IDV.  The Hyrax HDF4 and HDF5 handlers make data products display properly using popular visualization tools. 

We are excited and pleased to announce HDF5-1.10.0, the most powerful version of our flagship software ever.> This major new release of HDF5 is more powerful than ever before and packed with new capabilities that address important data challenges faced by our user community. HDF5 1.10.0 contains many important new features and changes, including those listed below. The features marked with * use new extensions to the HDF5 file format. The Single-Writer / Multiple-Reader or SWMR feature enables users to read data while concurrently writing it. * The virtual dataset (VDS) feature enables users to access data in a collection of HDF5 files as a single HDF5 dataset and to use the HDF5 APIs to work with that dataset. *   (NOTE:...

Francesc Alted, Freelance Consultant, HDF guest blogger

The HDF Group has a long history of collaboration with Francesc Alted, creator of PyTables.  Francesc was one of the first HDF5 application developers who successfully employed external compressions in an HDF5 application (PyTables). The first two compression methods that were registered with The HDF Group were LZO and BZIP2 implemented in PyTables; when Blosc was added to PyTables, it became a winner.

While HDF5 and PyTables address data organization and I/O needs for many applications, solutions like the Blosc meta-compressor presented in this blog, are simpler, achieve great I/O performance, and are alternative solutions to HDF5 in cases when portability and data organization are not critical, but compression is still desired.  Enjoy the read!

Why compression?

Compression is a hot topic in data handling. The largest database players have recently (or not-so-recently) implemented support for different kinds of compression libraries. Why is that? It’s all about efficiency: modern CPUs are so fast in comparison with storage write speeds that compression not only offers the opportunity to store more with less space, but to improve storage bandwidth also:

The HDF5 library is an excellent example of a data container that supported out-of-the-box compression in the very first release of HDF5 in November 1998. Their innovation was to introduce support for compression of chunked datasets in a way that permitted the developer to apply compression to each of the chunks individually, resulting in reasonably fast and transparent compression using different codecs. HDF5 also introduced pluggable compression filters that allowed external developers to implement support for different codecs for HDF5. Then with release 1.8.11, they added the ability to discover, load and register filters at run time. More recently, in release 1.8.15 (and fully documented in 1.8.16), HDF5 has a new Plugin Interface that provides a complete programmatic control of dynamically loaded plugins. HDF5’s filter features now offer much-desired flexibility, giving users the freedom to choose the codec that best suits their needs.

Why Blosc?

In the last decade the trend has been to implement faster codecs at the expense of reduced compression ratios. The idea is to reduce compression/decompression time overhead

Gerd Heber, The HDF Group and Haymo Kutschbach,* ILNumerics

Metaphorically speaking, this blog post is about a frog trying to climb out of a well, a damp and unsightly corner of the HDF5 ecosystem called HDF5.NET. People who know more about its genesis tell us that it was never intended as what it became to be perceived as, an “aspirational” .NET interface for HDF5 that would one day be complete and fully supported. Be that as it may, it’s important to ask, “What can we do today to better serve the needs of the .NET community?” We believe, as the title suggests, we need to take a step back to move forward.