David Dotson, doctoral student, Center for Biological Physics, Arizona State University; HDF Guest Blogger Recently I had the pleasure of meeting Anthony Scopatz for the first time at SciPy 2015, and we talked shop. I was interested in his opinions on MDSynthesis, a Python package our lab has designed to help manage the complexity of raw and derived […]
Mohamad Chaarawi, The HDF Group Second in a series: Parallel HDF5 In my previous blog post, I discussed the need for parallel I/O and a few paradigms for doing parallel I/O from applications. HDF5 is an I/O middleware library that supports (or will support in the near future) most of the I/O paradigms we talked
John Readey, The HDF Group Editor’s Note: Since this post was written in 2015, The HDF Group has developed HDF Cloud, a new product that addresses the challenges of adapting large scale array-based computing to the cloud and object storage while intelligently handling the full data management life cycle. If this is something that interests
HDF adds structure, context, and organization to the data to create information that can be shared…
Ted Habermann, The HDF Group “A strong foundation is being built for sharing data and information to create community knowledge and wisdom. This foundation includes HDF5 as the data layer with community conventions and ISO metadata facilitating use and understanding.” We have experienced so many monumental technological shifts during the last several decades that, like
HDF Group is hosting a one-day workshop at the upcoming Federation for Earth Science Information Partners (ESIP) Summer Meeting in Asilomar, CA on July 14th. Please join us to learn about new HDF tools, projects and perspectives. There will also be an HDF Town Hall meeting on Wednesday afternoon July 15th
* With Python’s Help Gerd Heber, The HDF Group Before the recent release of our PyHexad Excel add-in for HDF5[1], the title might have sounded like the slogan of a global coffee and baked goods chain. That was then. Today, it is an expression of hope for the spreadsheet users who run this country and
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.
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
