Innovation

Why do we use HDF5? We moved to HDF5 for our simulation data in 2016 from using our own proprietary file format. HDF5 had been on our radar for some time and we spent a couple of years investigating it and other file formats before deciding which we should switch to. HDF5 met all the criteria we had at the time. Amongst the criteria were: performance in speed and size, an accepted standard for scientific data, being open source, providing additional tools....

With MyIRE and HDF, everyone from a doctor in a small-town office to big data genetics researchers can work together to find new and powerful insights across data sets using a common set of tools - and do so in a repeatable way. And, because of HDF5, any user—whether large or small—is powered by the same technology used by CERN and NASA. We knew we wanted all of MyIRE’s users to have the power of NASA in their pocket. HDF5 made that possible....

Reproducibility in computing has important impacts in data sets of all shapes and sizes.  HDF provides high throughput interfaces to data sets in a reproducible way. Using HDF allows MyIRE users to analyze data across experiments and domains.  Methods, techniques, and case studies for using and increasing access to HDF will be presented. Please join us for a webinar with the team at MyIRE on Tuesday, February 12th, at 10:00 a.m. CST. Register now!...

Question and answers from the HDF5 C++ Webinar on January 24th, 2019. Read the followup questions and answers from presentations on H5CPP from Steven Varga, h5cpp Wrapper from Martin Shetty and Eugen Wintersberger and Ntuple: Tabular Data in HDF5 with C++ from Chris Green and Marc Paterno. ...

Join The HDF Group for a webinar consisting of three short presentations from the HDF5 community to learn about different approaches and exciting work being done by the HDF5 C++ community members. Steven Varga, Martin Shetty and Eugen Wintersberger, and Marc Paterno and Chris Green will share their vision for and experiences using HDF5 with C++....

A few years ago, I was looking for a data format with low latency block and stream support. While protocol buffers offered streams, it was lacking indexed block access. Soon, I realized I was looking for a container with file system-like properties. When I examined HDF5, I found it was very close to what I needed to store massive financial engineering datasets...

On March 30th, we announced the release of HDF5 version 1.10.2. With this release, we accomplished all the tasks planned for the major HDF5 1.10 series. It is time for applications to start migrating (or start their migration) from HDF5 1.8 to the new major release as we will be dropping support for HDF5 1.8 in Summer 2019. In this blog post , we will focus only on the major new features and bug fixes in HDF5 1.10.2. Hopefully, after reading about those, you will be convinced it is time to upgrade to HDF5 1.10.2....