About Peter Braam

I do research and I offer consulting.

As a consultant, my most important contributions are those of a thinking partner. I have often analyzed and documented strategic approaches or their tradeoffs and assisted with the creation of sofware and systems architecture, both for academic and commercial projects. I have been employed in technical executive positions to perform similar work. Sample projects in which I have assisted are described on this site.

My research is in the area of systems software and machine learning. I am interested in hardware and software architectures for performance and scalability and in novel roles for modern programming languages in creating complex parallel software. In the area of machine learning, I focus on understanding the mathematics of ML as a scientific method, for example through studying the interpretabilty of models.

I pursued an academic career until my mid thirties, and was described as a “see-er” more than a “do-er”. Being one of Sir Michael Atiyah’s last graduate students, my research focused on areas of gauge theory, topology and geometry. I remained in Oxford as a tenured faculty member until 1997.

My computing activities started in Oxford, and took me to Carnegie Mellon University in 1996. My strength is in alternative approaches for relatively complex problems. After a few years at CMU, I began to focus on my startup companies in storage and on being an executive after these were acquired. I created the Lustre file system, a widely used storage product for scientific computing. In 2013 I returned to more academic work, primarily working with Cambridge University on the design of computing for the SKA radio telescope. Since 2019, I am a Visiting Professor in the Department of Physics in Oxford University, and a Visiting Scientist in the Center of Computational Astrophysics at the Flatiron Institute in New York.

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Recent Activities

My current activities include research in the area of machine learning. I am interested in the mathematics of interpretability and of machine learning as a scientific method. In the area of systems green field approaches to extreme scale computing have my attention, inpired by the SKA compute problems about which I learned during the last five years. With the programming language community I exploring concepts for hybrid memory systems.

Lustre File System

In 1995 I started to work on distributed file systems, and - unexpectedly - in 1999 an opportunity emerged to design a new cluster file system which I named Lustre. Guided almost entirely by the requirements of the largest US government laboratories I led its architecture until 2008. Hundreds of individuals and hundreds of companies have made important contributions to it. It is now a cornerstone of scientific computing as one of the most used HPC software packages.


The square kilometer array (SKA) radio telescope needs serious, data intensive computing both to convert the input from the antennas into usable scientific data and to subsequently analyze that data. I work with the SKA Science Data Processing (SDP) group in the Cavendish Laboratory at Cambridge University to develop an architecture to approach these problems, and I advise the SDP project on other matters. A more detailed page about this can be found on this site.

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