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May
28
2015

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 been made to the HDF5 configuration Unfortunately, parallel HDF5 enhancement has been postponed This version contains a fix for an issue which occurred when building HDF5 within the source code directory. Check our downloads page for more information. We are still on target for releasing HDF5-1.10.0 next week, let us know if you have any comments! The HDF Group is committed to meeting our users' needs and expectations for...

May
07
2015

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. ...

Apr
23
2015

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 filters provided by the HDF5 library (“predefined filters,”) which include several types of filters for data compression, data shuffling and checksum. Users can implement their own “user-defined filters” and employ them with the HDF5 library. [caption id="attachment_10741" align="alignright" width="300"] Cars in a 1973 Philadelphia junkyard – image from National Archives and Records Administration[/caption] While the programming model and usage of the compression filters is straightforward, it is possible for...

Apr
16
2015

...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....

Apr
09
2015

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 by analysis applications, to checkpoint application memory to guard against system failure, to exercise out-of-core techniques for data that does not fit in a processor’s memory, and so on.  I/O middleware libraries, such as HDF5, provide application users with a rich interface for I/O access to organize their data and store it efficiently.  A lot of effort is invested by such I/O libraries to reduce or completely hide the cost of I/O from applications.

Parallel I/O is one technique used to access data on disk simultaneously from different application processes to maximize bandwidth and speed things up. There are several ways to do parallel I/O, and I will highlight the most popular methods that are in use today.

Blue Waters supercomputer at the National Center for Supercomputing Applications, University of Illinois, Urbana-Champaign campus. Blue Waters is supported by the National Science Foundation and the University of Illinois.

First, to leverage parallel I/O, it is very important that you have a parallel file system;

Apr
02
2015

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...