A Low Cost High Performance Computing Platform for Cellular Nonlinear Networks Using Python for CUDA

A novel platform (hardware and software) for complex systems modeling is proposed. It exploits the newest developments in both software (Continuum's - Anaconda's Numba and Numbapro Python packages) and hardware (the use of parallel computation on GPU provided by the CUDA computing platform) to ensure high-performance, high-productivity and high-portability in developing and simulating models of cellular nonlinear networks (CNN). A particular example is given in this paper for the case of Reaction Diffusion CNN and its effectiveness it is analyzed. It is shown that the hardware resources are effectively exploited while using a programming style closer to scientific computing and with a short learning cycle. With our low cost implementation we were able to achieve very good performance in implementing a Reaction-Diffusion CNN (about 500 Mcells/second). The platform can be easily extended to support a broader spectrum of computational models similar to CNNs, such as the Finite Difference Time Domain models for various physical processes.