A reduced concurrent memory access method to accelerate the computation of the lineal path function on large microstructures

Abstract

The Concurrent Reduced Memory Access method (CRMA) is a scalable memory-efficient Monte Carlo method for computing the lineal path function. It addresses an inherent memory bottleneck of lineal path function algorithms by utilising known properties of the two-point correlation function to reduce the number of voxels where the phase value must be evaluated. The CRMA method reduces the computation time and improves the scalability characteristics of the traditional lineal path function Monte Carlo methods. CRMA also provides additional information useful for analysing microstructures since the two-point correlation function is computed as part of the method. The CRMA method offers an efficient, scalable and extendable solution for computing the lineal path function.