Ulrich Drepper


Ulrich Drepper joined Red Hat again in 2017, after a seven-year hiatus. He is and during his previous stint also was a member of the office of the CTO. In that capacity, he is looking at upcoming technologies for products and internal application. His focus is mostly on machine learning and low-level/high-performance computing as well as alternative compute architectures.

Previously he worked at Goldman Sachs in the technology division. The last position was as a member of the data science research group, focusing on the development of models and various types of stochastic algorithms to aid in the operation of the technology division.

His main interests are in the areas of low-level technologies like machine and processor architectures, programming language, compilers, high-performance, and low-latency computing. In addition, he is interested in using statistics and machine learning for performance analysis or programs and security of application and OS environments. He worked on several revisions of the POSIX standard and was invited expert for both the C and C++ standard committees.

Ulrich received his Diploma in Informatics from the University of Karlsruhe, Germany.

Homepage: http://www.akkadia.org/drepper/.



Intelligent Application Configuration Data Management (2015)

It was a big step to allow programs to be customized for a specific situation by having configurations represented user-changable way. Unfortunately not much changed, especially for most non-GUI apps. The latter often have only very crude ways of steering once the process is running. In this talk I am showing how to create interfaces to a program’s configuration which provide a lot more flexibility than static files in /etc or whatever the equivalent is for the OS of your choice.


The Cost Of 64-bit Pointers (2015)

For many people “64 bits” are just plainly better than “32 bits”. The former is certainly a more recent development and, as such, has the potential of providing benefits. But not every program will unconditionally benefit. Measurements which show advantage often conflate the effects of multiple changes. In this talk we are looking at the individual changes of the ABIs, in particular the pointer size change, the contributions of the changes to performance, concentrating on the x86 architectures. I am showing ways to optimize performance with various techniques which can be selected to match the specific application. Some of the techniques will also be usable for non-x86 architectures.