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We discuss the current development of MC/DC (Monte Carlo Dynamic Code). MC/DCis primarily designed to serve as an exploratory Python-based MC transportcode. However, it seeks to offer improved performance, massive scalability, andbackend portability by leveraging Python code-generation libraries andimplementing an innovative abstraction strategy and compilation scheme. Here,we verify MC/DC capabilities and perform an initial performance assessment. Wefound that MC/DC can run hundreds of times faster than its pure Python mode andabout 2.5 times slower, but with comparable parallel scaling, than thehigh-performance MC code Shift for simple problems. Finally, to furtherexercise MC/DC's time-dependent MC transport capabilities, we propose achallenge problem based on the C5G7-TD benchmark model.

Development of MC/DC: a performant, scalable, and portable Python-based Monte Carlo neutron transport code