Hermes 0.9.8-beta release

Hermes 0.9.8 has been released. This release features tagging. Tags enable users to semantically define associations between blobs and provide an intuitive way of locating blobs which are related.

Liberating Real-Time Data via HDF5: The Fastest Approach for Exposing Embedded Data for Analysis, Machine Learning, and Cloud-Enabled Services

The HDF Group’s technical mission is to provide rapid, easy and permanent access to complex data. FishEye’s vision is “Synthesizing the world’s real-time data”. This white paper is intended for embedded system users, software engineers, integrators, and testers that use or want to use HDF5 to access, collect, use and analyze machine data. FishEye has developed an innovative process that provides the most efficient method to expose data from embedded systems that simplifies and liberates data for real-time analysis, machine learning, and cloud-enabled services.

An I/O Study of ECP Applications

We are pleased to post this white paper from The HDF Group intern, Chen Wang. This paper looks at the steps of analyzing and tuning the HACC-IO benchmarks, the impact of different access patterns, stripe settings and HDF5 metadata. It also compares the five benchmarks on two different parallel file systems, Lustre and GPFS and

C++ has come a long way and there’s plenty in it for users of HDF5

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….

Handling (and ingesting) data streams at 500K mess/s

By Francesc Alted. He is a freelance consultant and developing author of different open source libraries like PyTables, Blosc, bcolz and numexpr and an experienced programmer in Python and C. Francesc collaborates regularly with the The HDF Group in different projects. We explain our solution for handling big data streams using HDF5 (with a little help

ExaHyPE goes HDF5

Tobias Weinzierl, Durham University, UK, Sven Köppel, FIAS, Germany, Michael Bader, TUM, Germany, HDF Guest Bloggers ExaHyPE develops a solver engine for hyperbolic differential equations solved on adaptive Cartesian meshes. It supports various HDF5 output formats. Exascale computing is expected to allow scientists and engineers to simulate, and ultimately understand, wave phenomena with unprecedented accuracy

HDF5 Data Compression Demystified #2: Performance Tuning

Internal compression is one of several powerful HDF5 features that distinguish HDF5 from other binary formats and make it very attractive for storing and organizing data. Internal HDF5 compression saves storage space and I/O bandwidth and allows efficient partial access to data. Chunk storage must be used when HDF5 compression is enabled.

HDF5 Implementation in Mathematica

Scot Martin, Harvard University, HDF Guest Blogger HDF5 storage is really interesting. To me, its format has no fixed structure, but instead is based on introspection and discovery. Seems great to me; Mathematica has its origins first in artificial intelligence, so we ought to be able to do something here.  Approaching twenty-two years with Mathematica

HDF5 under the SOFA – A 3D audio case in HDF5 on embedded and mobile devices

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

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