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Aug
27
2015

Quincey Koziol, The HDF Group

“A supercomputer is a device for turning compute-bound problems into I/O-bound problems.” – Ken Batcher, Prof. Emeritus, Kent State University.

HDF5 began out of a collaboration between the National Center for Supercomputing Applications (NCSA) and the US Department of Energy’s Advanced Simulation and Computing Program (ASC), so high-performance computing (HPC) I/O has been in our focus from the very beginning.  As we are starting our 20th year of development on HDF5, HPC I/O continues to be a critical driver of new features.

Los Alamos National Laboratory is home to two of the world’s most powerful supercomputers, each capable of performing more than 1,000 trillion operations per second. Here, ASC is examining the effects of a one-megaton nuclear energy source detonated on the surface of an asteroid. Image from ASC at http://www.lanl.gov/asci/

The HDF5 development team has focused on three things when serving the HPC community: performance, freedom of choice and ease of use.

Aug
21
2015

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 data sets from molecular dynamics simulations, about which I was

Mohamad Chaarawi, The HDF Group

Second in a series: Parallel HDF5

NERSC’s Cray Sonexion system provides data storage for its Mendel scientific computing cluster.

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 about.

In this blog post I will discuss how to use HDF5 to implement some of the parallel I/O methods and some of the ongoing research to support new I/O paradigms. I will not discuss pros and cons of each method since we discussed those in the previous blog post.

But before getting on with how HDF5 supports parallel I/O, let’s address a question that comes up often, which is,

“Why do I need Parallel HDF5 when the MPI standard already provides an interface for doing I/O?”

Aug
11
2015

Mohamad Chaarawi, The HDF Group

Second in a series: Parallel HDF5

NERSC’s Cray Sonexion system provides data storage for its Mendel scientific computing cluster.

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 about.

In this blog post I will discuss how to use HDF5 to implement some of the parallel I/O methods and some of the ongoing research to support new I/O paradigms. I will not discuss pros and cons of each method since we discussed those in the previous blog post.

But before getting on with how HDF5 supports parallel I/O, let’s address a question that comes up often, which is,

“Why do I need Parallel HDF5 when the MPI standard already provides an interface for doing I/O?”

Aug
05
2015

The current improvement of using collective I/O to reduce the number of independent processes accessing the file system helped to improve the metadata reads for cgp_open substantially, yielding 100-1000 times faster execution times over the previous implementation....

Jul
30
2015

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 you, we’d love to hear from you.

 

Interestingly enough, in addition to being known as the place to go for BBQ and live music, Austin, Texas is a major hub of Python development.  Each year, Austin is host to the annual confab of Python developers known as the SciPy Conference.  Enthought, a local Python-based company, was the major sponsor of the conference and did a great job of organizing the event.  By the way, Enthought is active in Python-based training, and I thought the tutorial sessions I attended were very well done.  If you would like to get some expert training on various aspects of Python, check out their offerings.

As a first-time conference attendee, I found attending the talks and tutorials very informative and entertaining.  The conference’s focus is the set of packages that form the core of the SciPy ecosystem (SciPy, iPython, NumPy, Pandas, Matplotlib, and SymPy) and the ever-increasing number of specialized packages around this core.     

Jul
24
2015

Ted Habermann, The HDF Group

I first heard of HDF during the “Data Format Wars” of the 1990’s. These “battles” centered on the selection of a format for the emerging NASA Earth Observing System archives, and there were a number of contenders. HDF won that battle in the end because of the inherent flexibility of the format and the tools for reading and writing it.

Now, twenty years later, HDF has emerged as the foundation format for an incredibly diverse and growing selection of scientific and commercial disciplines.

Is it the inherent flexibility of the format that has led to this success? Maybe, but I would pick information integration as the killer HDF feature. 

Jul
16
2015

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 the diurnal cycle of light and dark, the technology life cycle (shown below) is becoming instinctual.

It starts with a new idea, (usually aimed at new customers), that destroys existing organizational expertise and threatens the continued existence of established processes and organizations. These disruptions raise a variety of difficult questions and initiate an Era of Ferment during which established enterprises gauge the impact of the disruption in their worlds and try to adjust. The ferment creates uncertainty, high risk, considerable wasted resources, and no interoperability.

The technology lifecycle

The ferment is ended when the community agrees on a dominant design and works together to make the design work.  Instead of deciding what they are going to do, they work to make what they are going to do better.  

Jun
15
2015

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...