Virtual prototyping of complex optical systems on multiprocessor workstations

Abstract

Nowadays, optical designers often use multiprocessor workstations for the virtual prototyping of complex optical systems. Modern workstations may have several CPUs with up to 128 virtual cores each and a non-uniform access speed to different memory areas. Effective implementation of virtual prototyping methods and algorithms requires the development of special methods for efficient algorithm parallelization and shared memory access. As a basis for the virtual prototyping, we proposed a progressive backward photon mapping method that allows for reducing the amount of data used by photon maps, speeding up the luminance calculation process, and estimating the luminance errors for the resulting image. The main algorithmic complexity of this method is the need to synchronize data when calculating and accumulating the luminance of indirect and caustic illumination. The authors propose the three-level semi-synchronous parallelization method, which consists of fully synchronous, semi-synchronous, and asynchronous levels with illumination processing effectively distributed among the computation threads. The main benefit of the developed method is that it does not require additional synchronization when accumulating luminance, thus increasing the image synthesis speed. The designed three-level method can also be used in distributed systems with good scalability. The results obtained with virtual prototypes of complex optical systems are presented.

Effective caustics simulation for real-time rendering applications

“…In previous works, authors have found that the fastest methods to calculate the caustic illumination with CPU resources are methods based on forward or backward photon mappings 20 . Among them the backward photon mapping method uses less memory and works faster on a single thread, however, its parallel implementation is more complex 21 . The flow chart for the progressive backward photon mapping rendering algorithm is shown in Figure 3.…”