Fig 5. Tissue Forge performance metrics for windowless and real-time rendering modes.

A: Computational cost per time step per particle for varying number of particles, varying cutoff distance and varying architecture when running windowless with fixed particle density and one implicit interaction. Computational cost generally increases with increasing cutoff distance. On a CPU, computational cost is lowest near 10k particles and then begins to increase. When offloading implicit interactions to a GPU, computational cost is generally less and tends towards a constant value. B. Computational cost for varying number of potentials defining implicit interactions with 10M particles and a cutoff of 5. Multiple potentials were implemented using potential arithmetic. Computational cost generally increases linearly with increasing number of potentials. C. Representative cost of solver stages when executing simulations from panel A with a cutoff of 5 in windowless (left) and real-time rendering (right) modes with implicit interactions calculated on a CPU (top) and GPU (bottom). Windowless mode simulated 10M particles. Real-time rendering mode simulated 10k particles. Size of bars for each mode represents relative cost of a simulation step. Bars are divided by solver stages that are ordered by execution order, and the area of each represents the portion of the total cost that the stage contributes. In both modes and architectures, force calculations make up the majority of the computational cost. As demonstrated in A, computing performance on a GPU is more efficient with increasing number of particles (Windowless), whereas computing performance on a CPU is more efficient for few particles (Real-time rendered).