Alea.cuBase and F#: a new level of GPU Computing [Joint event with GPU Meetup]

Update: for this special event, we'll have a special joint meeting with the HPC & GPU Silicon Valley group (https://www.meetup.com/HPC-GPU-Supercomputing-Group-of-Silicon-Valley/events/88706612/), at the San Jose GPU Conference. Dr Daniel Egloff will be giving a presentation on writing GPU applications with Alea.cuBase and F#. There will be two other talks, with Dr. Ian Buck, NVIDIA, and Dr. Lorena Barba, Boston University - we're looking forward to seeing you at what should be a very exciting evening! More information regarding logistics will follow, so stay tuned!

If you are interested in carpooling from San Francisco, look in the comments section.

Dr. Daniel Egloff's talk

Alea.cuBase is a complete solution to develop CUDA accelerated applications on .NET. It produces highly optimized GPU code from F# and enhances the productivity with rapid prototyping inside the F# interactive. Even more interesting is the capability to dynamically generate GPU code and compile it run-time. Alea.cuBase and F# are therefore an ideal combination to develop GPU aware domain specific languages. In our presentation we give a brief overview of CUDA and GPU computing and introduce the basic concepts of Alea.cuBase. We then move on to live coding examples, which show the full potential of Alea.cuBase.

All the attendees of the talk will get access to a free personal noncommercial license of Alea.cuBase (https://www.quantalea.net/products/introduction/)!

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The Speaker

Dr Daniel Egloff studied mathematics, theoretical physics and computer science at the University of Zurich and the ETHZ. He has a PhD in mathematics from the University of Fribourg, Switzerland. In 2008 he set up his own software engineering and consulting company and founded QuantAlea (https://www.quantalea.net/) by the end of 2009. Since then he advised several high profile clients on quantitative finance, software development and high performance computing. Over the last few years he has become a well-known expert in GPU computing and parallel algorithms and successfully applied GPUs in productive systems for derivative pricing, risk calculations and statistical analysis. Before setting up his own company he had spent more than fifteen years in the financial service industry, where his work revolved around derivative pricing, risk management with a special focus on market and credit risk and high performance computing on clusters and grids.

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