Elena Pourmal and Quincey Koziol – The HDF Group UPDATE: Check our support pages for the newest version of HDF5-1.10.0. Concurrent Access to an HDF5 File: Single Writer / Multiple Reader (SWMR) Virtual Dataset (VDS) Scalable Chunk Indexing Persistent Free Filespace Tracking Collective Metadata I/O Integration of Java HDF5 JNI into HDF5 Many changes have […]
Large, rich and complex collections of HDF data can be filtered and viewed with the help of OPeNDAP. HDF data can be provided in manageable servings, on demand in real time, inexpensively, even on the user’s desktop or mobile device.
When storing data, the rich, portable metadata capabilities, including directed graph structures (e.g., hierarchies), complex attributes, and inter-object references make HDF5 a superior choice for maintaining the bond between data and metadata at the lowest level. Community involvement is an essential part of the HDF Group’s mission: It is vital to sustaining the business and is our brain trust when making decisions about changes to HDF5, setting priorities, and adding new features.
Elena Pourmal, The HDF Group What happened to my compression? One of the most powerful features of HDF5 is the ability to compress or otherwise modify, or “filter,” your data during I/O. By far, the most common user-defined filters are ones that perform data compression. As you know, there are many compression options. There are
…we focus on how far we can push our personal computing devices with Spark. It consists of 7,850 HDF-EOS5 files covering 27 years and totals about 120 GB. We use a driver script, which reads a dataset of interest from each file in the collection, computes per-file quantities of interest, and gathers them in a CSV file for visualization. The processing time on our reference tablet machine for 3.5 years of data using 4 logical processors was about 10 seconds.
Mohamad Chaarawi, The HDF Group First in a series: parallel HDF5 What costs applications a lot of time and resources rather than doing actual computation? Slow I/O. It is well known that I/O subsystems are very slow compared to other parts of a computing system. Applications use I/O to store simulation output for future use
HDF Group has just announced “HDF Server” – a freely available service that enables remote access to HDF5 content using a RESTful API. In our scenario, using HDF Server, we upload our Monopoly simulation results to the server and then interested parties can make requests for any desired content to the server – no file size issues, no downloading entire files…
Andrew Collette, Research Scientist with IMPACT, HDF Guest Blogger “…HDF5 is that rare product which excels in two fields: archiving and sharing data according to strict standardized conventions, and also ad-hoc, highly flexible and iterative use for local data analysis. For more information on using Python together with HDF5…” An enormous amount of effort has
HDF has been the primary format for NASA Earth Observing System (EOS) satellite data products for more than fifteen years. The HDF Earth Science Program supports EOS and other significant earth science monitoring activities.
The workshop program has two main tracks, one on HPC-oriented technologies that support the industry, and one on oil & gas technologies and how they can leverage HPC.
