The research paper “A path-level exact parallelization strategy for sequential simulation” by Oscar F. Peredo (Computer Architecture Department, UPC-BarcelonaTech, Spain; Telefonica I+D, Chile), Daniel Baeza (Advanced Laboratory for Geostatistical Supercomputing, University of Chile, Chile), Julian M. Ortiz (The Robert M. Buchan Department of Mining, Queen’s University, Canada), and Jose R. Herrero (Computer Architecture Department, UPC-BarcelonaTech, Spain) was recently published in Computers & Geosciences.

You can find it here.

Abstract
Sequential Simulation is a well known method in geostatistical modelling. Following the Bayesian approach for simulation of conditionally dependent random events, Sequential Indicator Simulation (SIS) method draws simulated values for K categories (categorical case) or classes defined by K different thresholds (continuous case). Similarly, Sequential Gaussian Simulation (SGS) method draws simulated values from a multivariate Gaussian field. In this work, a path-level approach to parallelize SIS and SGS methods is presented. A first stage of re-arrangement of the simulation path is performed, followed by a second stage of parallel simulation for nonconflicting nodes. A key advantage of the proposed parallelization method is to generate identical realizations as with the original non-parallelized methods. Case studies are presented using two sequential simulation codes from GSLIB: SISIM and SGSIM. Execution time and speedup results are shown for large-scale domains, with many categories and maximum kriging neighbours in each case, achieving high speedup results in the best scenarios using 16 threads of execution in a single machine.