A Flexible Strategy for Distributed and Parallel Execution of a Monolithic Large-Scale Sequential Application Felipe Navarro 1 , Carlos Gonz´alez 1 , ´ Oscar Peredo 1 , Gerson Morales 1 , ´ Alvaro Ega˜ na 1 , and Juli´an M. Ortiz 1,2 1 ALGES Laboratory, Advanced Mining Technology Center (AMTC), University of Chile, Chile 2 Department of Mining Engineering, University of Chile, Chile Abstract. A wide range of scientific computing applications still use al- gorithms provided by large old code or libraries, that rarely make profit from multiple cores architectures and hardly ever are distributed. In this paper we propose a flexible strategy for execution of those legacy codes, identifying main modules involved in the process. Key technologies in- volved and a tentative implementation are provided allowing to under- stand challenges and limitations that surround this problem. Finally a case study is presented for a large-scale, single threaded, stochastic geo- statistical simulation, in the context of mining and geological modeling applications. A successful execution, running time and speedup results are shown using a workstation cluster up to eleven nodes. Keywords: HPC, parallel computing, distributed system, workload mod- eling, gslib. 1 Introduction The development of scientific computing applications has been benefited by new hardware technologies and software frameworks, allowing new applications to reach faster execution times, using better programming practices. Despite these advances, many fields in science and engineering still use algorithms and methods implemented in large monolithic applications, in the sense that they have single- tiered and self-contained software designs, contrary to current trends of modular and flexible designs. From those monolithic applications, only a portion were designed to efficiently use multi-core architectures and even less can be executed in distributed environments. Nowadays, many monolithic sequential applications are still actively used, taking several minutes, hours or days to compute. Many scientists are not parallel computing users and –in some cases – have basic programming skills. Most of the time they use large old code or libraries designed for single-core workstations, mostly because their research priority is G. Hern´ andez et al. (Eds.): CARLA 2014, CCIS 485, pp. 54–67, 2014. c  Springer-Verlag Berlin Heidelberg 2014