PMC

The mesh sizes used for the FTCS method varied between the LR1 and HRD model. LR1 program code was generated using mesh sizes of up to 100 × 100, while HRD code generation used meshes of up to 60 × 60. For the Crank-Nicolson method, the same set of meshes is used for both models in generating code. The total number of equations is approximately equal to the number of equations in the model multiplied by the number of nodes in the mesh. Generation time of simulation code using explicit methods is in minutes while it takes hours to generate code using implicit methods.

Simplified code generator class diagram.

Our custom-designed Web interface of the QR code generator and real-time rapid diagnostic test (RDT) map (available at http://devpc02.ee.ucla.edu/rdtonglass). (a) QR code generator takes in and stores the information for one or more RDTs and produces a QR code identifier to be printed and attached to each physical test. (b) RDTs processed by each user can be viewed on this Google Maps-powered interface. Results can be sorted/searched by various attributes including disease type, test location, time and date, RDT type, etc. Image Credit: Google Maps, Imagery ©2014, Digital Globe, U.S. Geological Survey, USDA Farm Service Agency.

(A) Building time of the cortical microcircuit model simulated with NEST and with NeuronGPU on a system equipped with an Intel Core i9-9900K CPU. (B) Times for code generation, compilation, and initialization of the cortical microcircuit model for GeNN. The first two phases are performed by the CPU, and the times refer to a system equipped with an Intel Core i9-9900K. The third phase is mainly performed by the GPU, and the figure shows the time for an NVIDIA Tesla V100. (C,D) Simulation times per second of biological time of the cortical microcircuit model simulated with NEST, NeuronGPU, and GeNN on various CPU and GPU hardware. (E) Contributions of neuron dynamic update time, Poisson generator time, and spike handling and delivery time to the total simulation time of the Potjans-Diesmann model, simulated using NeuronGPU and GeNN on a Tesla V100 GPU. In GeNN the Poissonian input signal is generated within the same code that manages the neuron's dynamics, and furthermore it was not possible to separate the time used for spike handling and delivery from the remaining contributions to the simulation time. The horizontal line represents the biological time. The simulation time step is set to 0.1 ms.