Boosting materials science simulations by high performance computing

Technology development is often limited by knowledge of materials engineering and manufacturing processes. This scenario spans across scales and disciplines, from aerospace engineering to MicroElectroMechanical Systems (MEMS) and NanoElectroMechanical Systems (NEMS). The mechanical response of materials is dictated by atomic/nanometric scale processes that can be explored by molecular dynamics (MD) simulations. In this work we employ atomistic simulations to prove indentation as a prototypical deformation process showing the advantage of High Performance Computing (HPC) implementations for speeding up research. Selecting the right HPC hardware for executing simulations is a process that usually involves testing different hardware architectures and software configurations. Currently, there are several alternatives, using HPC cluster facilities shared between several researchers, as provided by Universities or Government Institutions, owning a small cluster, acquiring a local workstation with a high-end microprocessor, and using accelerators such as Graphics Processing Units (GPU), Field Programmable Gate Arrays (FPGA), or Intel Many Integrated Cores (MIC). Given this broad set of alternatives, we run several benchmarks using various University HPC clusters, a former TOP500 cluster in a foreign computing center, two high-end workstations and several accelerators. A number of different metrics are proposed to compare the performance and aid in the selection of the best hardware architecture according to the needs and budget of researchers. Amongst several results, we find that the Titan X Pascal GPU has a ∼3 x speedup against 64 AMD Opteron CPU cores.Publicado en: Mecánica Computacional vol. XXXV, no. 10.Facultad de Ingenierí

Boosting materials science simulations by high performance computing

Technology development is often limited by knowledge of materials engineering and manufacturing processes. This scenario spans across scales and disciplines, from aerospace engineering to MicroElectroMechanical Systems (MEMS) and NanoElectroMechanical Systems (NEMS). The mechanical response of materials is dictated by atomic/nanometric scale processes that can be explored by molecular dynamics (MD) simulations. In this work we employ atomistic simulations to prove indentation as a prototypical deformation process showing the advantage of High Performance Computing (HPC) implementations for speeding up research. Selecting the right HPC hardware for executing simulations is a process that usually involves testing different hardware architectures and software configurations. Currently, there are several alternatives, using HPC cluster facilities shared between several researchers, as provided by Universities or Government Institutions, owning a small cluster, acquiring a local workstation with a high-end microprocessor, and using accelerators such as Graphics Processing Units (GPU), Field Programmable Gate Arrays (FPGA), or Intel Many Integrated Cores (MIC). Given this broad set of alternatives, we run several benchmarks using various University HPC clusters, a former TOP500 cluster in a foreign computing center, two high-end workstations and several accelerators. A number of different metrics are proposed to compare the performance and aid in the selection of the best hardware architecture according to the needs and budget of researchers. Amongst several results, we find that the Titan X Pascal GPU has a ∼3 x speedup against 64 AMD Opteron CPU cores.Publicado en: Mecánica Computacional vol. XXXV, no. 10.Facultad de Ingenierí

Faster inference from state space models via GPU computing

Funding: C.F.-J. is funded via a doctoral scholarship from the University of St Andrews, School of Mathematics and Statistics.Inexpensive Graphics Processing Units (GPUs) offer the potential to greatly speed up computation by employing their massively parallel architecture to perform arithmetic operations more efficiently. Population dynamics models are important tools in ecology and conservation. Modern Bayesian approaches allow biologically realistic models to be constructed and fitted to multiple data sources in an integrated modelling framework based on a class of statistical models called state space models. However, model fitting is often slow, requiring hours to weeks of computation. We demonstrate the benefits of GPU computing using a model for the population dynamics of British grey seals, fitted with a particle Markov chain Monte Carlo algorithm. Speed-ups of two orders of magnitude were obtained for estimations of the log-likelihood, compared to a traditional ‘CPU-only’ implementation, allowing for an accurate method of inference to be used where this was previously too computationally expensive to be viable. GPU computing has enormous potential, but one barrier to further adoption is a steep learning curve, due to GPUs' unique hardware architecture. We provide a detailed description of hardware and software setup, and our case study provides a template for other similar applications. We also provide a detailed tutorial-style description of GPU hardware architectures, and examples of important GPU-specific programming practices.Publisher PDFPeer reviewe

Monte Carlo Simulations of Settlement Dynamics in GPUs

Recently, a Monte Carlo model was proposed in order to simulate settlement dynamics in drylands, including several environmental factors, and it was implemented as a serial CPU code. In this work we present a parallel implementation of that code using graphics processing units (GPU) and NVIDIA CUDA. The code was tested with two experiments, a Baseline case and a Realistic case. We take advantage of the GPU architecture to obtain significant speedups: (Formula presented.) 8 (Formula presented.) to (Formula presented.) 20 (Formula presented.) with the Baseline case in a NVIDIA Tesla C2050 versus a Phenom 1055T CPU. The Realistic case obtained (Formula presented.) 80 (Formula presented.) of speedup in the same hardware. The GPU performance of the code will allow the inclusion of additional factors affecting settlements and large grid sizes for detailed environmental degradation models.Fil: Millán, Emmanuel Nicolás. Universidad Nacional de Cuyo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; ArgentinaFil: Goirán, Silvana Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Provincia de Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Universidad Nacional de Cuyo. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales; ArgentinaFil: Piccoli, María Fabiana. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; ArgentinaFil: García Garino, Carlos. Universidad Nacional de San Luis; ArgentinaFil: Aranibar, Julieta Nelida. Universidad Nacional de Cuyo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; ArgentinaFil: Bringa, Eduardo Marcial. Universidad Nacional de Cuyo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentin