by Scot Breitenfeld, Director of Engineering, The HDF Group For an experiment at the Advanced Photon Source, an aluminum specimen cracked from a corrosion pit to complete failure after 14,346 fatigue cycles. During the process, a micro-CT scanned the specimen, generating 16.8 GB HDF5 files per scan. For this study, we replayed four scans as […]
The HDF Group is happy to announce the release of HSDS 1.0.0, h5pyd 1.0.0, and hdf5-json 2.0.0. The three projects now share a single implementation of the HDF5 object model instead of maintaining three copies of it. Additionally, this release brings major architectural improvements, enhanced APIs, streamlined package dependencies, and a more robust foundation for future development. HSDS enables users
Nearly 40 years on, HDF4 receives one final official release focused on security, stability, and long-term use. HDF4 has supported scientific computing for nearly four decades. It has powered remote sensing systems, Earth science applications, aerospace work, and long-lived research and operational systems. HDF5 has been the focus of active development for many years, but
Excerpt: A new ecosystem-level audit identifies recurring failure classes, clarifies where authority and responsibility reside, and proposes a phased mitigation roadmap for HDF5 and its wider software and data ecosystem. HDF5 is foundational infrastructure for science and engineering. It stores experimental observations, simulation results, images, models, sensor data, and long-lived scientific records. That reach makes
In our July 31 post, “When an HDF5 file points outside itself,” we described the use of HDF5 external raw storage in the Hugging Face incident. Some later accounts have labeled this as an “HDF5 vulnerability.” A recent Black Hat presentation, around 27:13, refers to it as “a vulnerability in HDF5 dataset parsing” and “the
by Scot Breitenfeld, Director of Engineering, The HDF Group Training and deploying large models requires transferring significant volumes of low-precision tensors, including weights, gradients, activations, and KV caches. bfloat16 maintains float32’s exponent range, ensuring numerical stability with reduced storage requirements. FP8 E4M3 and E5M2 are standard for H100-class hardware, while FP6 and FP4 enable the
by Gerd Heber, Executive Director, The HDF Group When something goes wrong, someone is going to tell the story. You’ll be better off if it’s you. Otherwise, you create an opportunity for rumors, hearsay, and false information to spread. (Jason Fried & David Heinemeier Hansson, Rework) Reports on the July 2026 OpenAI agent intrusion have
HDF5 2.2.0 Now Available The HDF Group is pleased to announce the release of HDF5 2.2.0, delivering improved cloud performance, stronger security capabilities, enhanced build and deployment workflows, and numerous reliability and usability improvements across the HDF5 ecosystem. This release focuses on making HDF5 easier to deploy, more secure in production environments, and more efficient
In this episode of “Call the Doctor,” Aleksandar Jelenak explores the HDF5 Extension Proposal HEP001. This proposal aims to modernize how we handle tabular data in HDF5 by introducing a columnar storage specification, moving beyond legacy row-based approaches to enable better interoperability, performance, and metadata flexibility. Relevant Links HDF Alliance GitHub Repository HEP001 Proposal Document
In this session of “Call the Doctor,” The HDF Group’s Scot Breitenfeld discusses a major modernization effort for HDF5: moving from opaque integer-based filter parameters to human-readable TOML strings. This update, targeted for the HDF5 2.2 release, simplifies the developer experience for plugin authors and users of high-level languages like Python, Java, and Fortran. Configuring
