Random Numbers Aren’t Random

This workshop should be required for anyone who does arithmetic on a computer or builds algorithms around random sampling. Long ago when computational science was young, those of us working in the field learned numerical analysis. If we wanted an algorithm based on random sampling, we wrote our own random number generators. In other words, we were forced to take the time to understand how numbers work on computers.

Now days, these topics are rarely taught. In many ways, this is a good thing. There is so much to learn before you can do computational science, who has time to worry about numbers? We will talk about how numbers can fail you and how to avoid contributing to disaster or the embarrassment of publishing bad research results. There isn’t much you must know. We’ll cover it all in this workshop.

Meet the Instructor

Tim Mattson a parallel programmer obsessed with every variety of science. In 2023 he retired after a 45-year career in HPC (30 of which were with Intel). He has had the privilege of working with people much smarter than himself on great projects including: (1) the first TFLOP computer (ASCI Red), (2) Parallel programming languages … Linda, MPI, OpenMP, OpenCL, OCR and PyOMP (3) two different research processors (Intel's TFLOP chip and the 48 core SCC), (4) Data management systems (Polystore systems and Array-based storage engines), and (5) the GraphBLAS API for expressing graph algorithms as sparse linear algebra. Tim has over 150 publications including six books on different aspects of parallel computing.

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Co-hosted with: GradFUTURES

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