Alea.CUDA - Combining the computational power of GPUs with the functional elegance of F#
F# and GPUs are two trailblazing yet unrelated technologies. F# is a uniquely productive language to solve complex problems in a clear and concise way. On the other hand GPUs offer an immense computational power to solve large number crunching tasks fast and efficiently.
Our presentation shows how to wed the two technologies F# and GPUs with the help of Alea.CUDA. Alea.CUDA is our new framework and compiler service for GPU computing. It extends F# with the key CUDA concepts and allows to compile F# code quotations to an executable GPU code. I will briefly introduce Alea.CUDA and show you – by means of several live coding examples – how it can be used to develop GPU algorithms entirely in F# with the full flexibility of CUDA-C. Besides getting an understanding of the main features of Alea.CUDA you will become familiar with some of the basic GPU computing paradigms. To round off the presentation I shall reveal some of the implementational aspects of Alea.CUDA.
Dr Daniel Egloff studied mathematics, theoretical physics and computer science at the University of Zurich and the ETHZ (Zurich). 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 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 as well as high performance computing on clusters and grids.
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