Future exascale computers will have to be suitable for both data science applications and for more “traditional” modeling and simulation. However, data science applications are often posed as questions about discrete objects such as graphs while problems in modeling and simulation are usually stated initially in terms of classical mathematical analysis. We will present examples and arguments to show that the two points of view are not as distinct as one might think. Recognizing the connections between the two problem sets will be essential to development of algorithms capable of exascale performance. Our main examples will be from applications of Monte Carlo to attacking hard problems of the kind that occur both in data science and in computational modeling of physical phenomena. We will illustrate how taking ideas from both worlds pays wonderful dividends.
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