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The DataFlow paradigm has been existing for a long time, but good enough enabling technologies have been made available only very recently. The teaching of a different from the mainstream paradigm, such as the DataFlow paradigm, encounters a few problems. If a new paradigm is presented among experienced professionals, the presentation is often received with resistance. If a new paradigm is presented before students, the presentation is always received with enthusiasm, especially if tools and libraries that support the paradigm do exist. In order to overcome the problem with introducing the new paradigm, a series of courses was organized for Universities in the USA and Europe, to teach the DataFlow paradigm, using the Maxeler approach as the baseline for practical work. The course is organized in three different levels: (A) full-hour presentation that includes introducing the programming model with hands-on examples, which is most appropriate for research groups at Universities (B) a certified full-day short-course that students could use as an extension to diploma, (C) full-semester course for credits that is part of graduation requirement. This paper discusses different methods and their results, which could be used in order to efficiently introduce any different from the mainstream paradigm. The results presented in this paper are for 10 universities in the group A, 10 in the group B, and 10 in the group C. Where appropriate, the findings are backed with statistics data.

Particle-based reaction-diffusion models offer a high-resolution alternative to the continuum reaction-diffusion approach, capturing the discrete and volume-excluding nature of molecules undergoing stochastic dynamics. These methods are thus uniquely capable of simulating explicit self-assembly of particles into higher-order structures like filaments, spherical cages, or heterogeneous macromolecular complexes, which are ubiquitous across living systems and in materials design. The disadvantage of these high-resolution methods is their increased computational cost. Here we present a parallel implementation of the particle-based NERDSS software using the Message Passing Interface (MPI) and spatial domain decomposition, achieving close to linear scaling for up to 96 processors in the largest simulation systems. The scalability of parallel NERDSS is evaluated for bimolecular reactions in 3D and 2D, for self-assembly of trimeric and hexameric complexes, and for protein lattice assembly from 3D to 2D, with all parallel test cases producing accurate solutions. We demonstrate how parallel efficiency depends on the system size, the reaction network, and the limiting timescales of the system, showing optimal scaling only for smaller assemblies with slower timescales. The formation of very large assemblies represents a challenge in evaluating reaction updates across processors, and here we restrict assembly sizes to below the spatial decomposition size. We provide the parallel NERDSS code open source, with detailed documentation for developers and extension to other particle-based reaction-diffusion software.