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

Efficient Algorithms And Optimizations For Scientific Computing On Many-Core Processors

Designing efficient algorithms for many-core and multicore architectures requires using different strategies to allow for the best exploitation of the hardware resources on those architectures. Researchers have ported many scientific applications to modern many-core and multicore parallel architectures, and by doing so they have achieved significant speedups over running on single CPU cores. While many applications have achieved significant speedups, some applications still require more effort to accelerate due to their inherently serial behavior. One class of applications that has this serial behavior is the Monte Carlo simulations. Monte Carlo simulations have been used to simulate many problems in statistical physics and statistical mechanics that were not possible to simulate using Molecular Dynamics. While there are a fair number of well-known and recognized GPU Molecular Dynamics codes, the existing Monte Carlo ensemble simulations have not been ported to the GPU, so they are relatively slow and could not run large systems in a reasonable amount of time. Due to the previously mentioned shortcomings of existing Monte Carlo ensemble codes and due to the interest of researchers to have a fast Monte Carlo simulation framework that can simulate large systems, a new GPU framework called GOMC is implemented to simulate different particle and molecular-based force fields and ensembles. GOMC simulates different Monte Carlo ensembles such as the canonical, grand canonical, and Gibbs ensembles. This work describes many challenges in developing a GPU Monte Carlo code for such ensembles and how I addressed these challenges. This work also describes efficient many-core and multicore large-scale energy calculations for Monte Carlo Gibbs ensemble using cell lists. Designing Monte Carlo molecular simulations is challenging as they have less computation and parallelism when compared to similar molecular dynamics applications. The modified cell list allows for more speedup gains for energy calculations on both many-core and multicore architectures when compared to other implementations without using the conventional cell lists. The work presents results and analysis of the cell list algorithms for each one of the parallel architectures using top of the line GPUs, CPUs, and Intel’s Phi coprocessors. In addition, the work evaluates the performance of the cell list algorithms for different problem sizes and different radial cutoffs. In addition, this work evaluates two cell list approaches, a hybrid MPI+OpenMP approach and a hybrid MPI+CUDA approach. The cell list methods are evaluated on a small cluster of multicore CPUs, Intel Phi coprocessors, and GPUs. The performance results are evaluated using different combinations of MPI processes, threads, and problem sizes. Another application presented in this dissertation involves the understanding of the properties of crystalline materials, and their design and control. Recent developments include the introduction of new models to simulate system behavior and properties that are of large experimental and theoretical interest. One of those models is the Phase-Field Crystal (PFC) model. The PFC model has enabled researchers to simulate 2D and 3D crystal structures and study defects such as dislocations and grain boundaries. In this work, GPUs are used to accelerate various dynamic properties of polycrystals in the 2D PFC model. Some properties require very intensive computation that may involve hundreds of thousands of atoms. The GPU implementation has achieved significant speedups of more than 46 times for some large systems simulations

Molecular Dynamics Simulation of Iron — A Review

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Simulación de la evolución de defectos en materiales irradiados de interés en fusión nuclear mediante un método GPU-OKMC

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Ciencias Físicas, leída el 28-11-2022As the current world reliance on fossil fuels proves to have catastrophic environmental consequences, which are only exacerbated with a growing world economy and population, a future clean source of energy is required. The scientific community expects nuclear fusion to fulfill this task, in particular magnetically confined fusion. To achieve this, an experimental fusion reactor, the ITER Project, is underway and shall provide the basis for a future demonstration power plant, known as DEMO. One of the most important challenges in the design of a future nuclear fusion reactor is the choice of materials. Materials are subjected to an intense flux of neutrons and heat in a fusion reactor like ITER or, in a much more pronounced way, DEMO. Under irradiation, a large amount of defects are created and, as aconsequence, the properties of materials are severely degraded, and may cause the reactor components to malfunction or break...Dado que la actual dependencia mundial de los combustibles fósiles muestra ciertas consecuencias catastro cas para el medio ambiente, las cuales son magnificadas a medida que crecen la economía y población mundiales, se necesita una fuente de energía limpia para el futuro. La comunidad científica espera que sea la fusión nuclear la que desempeñe este papel, en particular la fusión por confinamiento magnético. Para ello, un reactor de fusión experimental, el Proyecto ITER, esta en marcha y proporcionará las bases para un futuro reactor de demostración llamado DEMO. Uno de los desafíos principales en el diseño de un futuro reactor de fusión es la elección de los materiales. En efecto, los materiales serán sometidos a un flujo intenso de neutrones y calor en un reactor de fusión como ITER; y, de forma más pronunciada, en uno como DEMO. Esto provocara la creación de una gran cantidad de defectos, por lo que las propiedades de los materiales serán gravemente alteradas, y podrán provocar que los componentes del reactor dejen de funcionar correctamente o, incluso, se quiebren...Fac. de Ciencias FísicasTRUEunpu

Pipelining the Fast Multipole Method over a Runtime System

Fast Multipole Methods (FMM) are a fundamental operation for the simulation of many physical problems. The high performance design of such methods usually requires to carefully tune the algorithm for both the targeted physics and the hardware. In this paper, we propose a new approach that achieves high performance across architectures. Our method consists of expressing the FMM algorithm as a task flow and employing a state-of-the-art runtime system, StarPU, in order to process the tasks on the different processing units. We carefully design the task flow, the mathematical operators, their Central Processing Unit (CPU) and Graphics Processing Unit (GPU) implementations, as well as scheduling schemes. We compute potentials and forces of 200 million particles in 48.7 seconds on a homogeneous 160 cores SGI Altix UV 100 and of 38 million particles in 13.34 seconds on a heterogeneous 12 cores Intel Nehalem processor enhanced with 3 Nvidia M2090 Fermi GPUs.Comment: No. RR-7981 (2012

Roadmap on Electronic Structure Codes in the Exascale Era

Electronic structure calculations have been instrumental in providing many important insights into a range of physical and chemical properties of various molecular and solid-state systems. Their importance to various fields, including materials science, chemical sciences, computational chemistry and device physics, is underscored by the large fraction of available public supercomputing resources devoted to these calculations. As we enter the exascale era, exciting new opportunities to increase simulation numbers, sizes, and accuracies present themselves. In order to realize these promises, the community of electronic structure software developers will however first have to tackle a number of challenges pertaining to the efficient use of new architectures that will rely heavily on massive parallelism and hardware accelerators. This roadmap provides a broad overview of the state-of-the-art in electronic structure calculations and of the various new directions being pursued by the community. It covers 14 electronic structure codes, presenting their current status, their development priorities over the next five years, and their plans towards tackling the challenges and leveraging the opportunities presented by the advent of exascale computing

Roadmap on Electronic Structure Codes in the Exascale Era

Electronic structure calculations have been instrumental in providing many important insights into a range of physical and chemical properties of various molecular and solid-state systems. Their importance to various fields, including materials science, chemical sciences, computational chemistry and device physics, is underscored by the large fraction of available public supercomputing resources devoted to these calculations. As we enter the exascale era, exciting new opportunities to increase simulation numbers, sizes, and accuracies present themselves. In order to realize these promises, the community of electronic structure software developers will however first have to tackle a number of challenges pertaining to the efficient use of new architectures that will rely heavily on massive parallelism and hardware accelerators. This roadmap provides a broad overview of the state-of-the-art in electronic structure calculations and of the various new directions being pursued by the community. It covers 14 electronic structure codes, presenting their current status, their development priorities over the next five years, and their plans towards tackling the challenges and leveraging the opportunities presented by the advent of exascale computing.Comment: Submitted as a roadmap article to Modelling and Simulation in Materials Science and Engineering; Address any correspondence to Vikram Gavini ([email protected]) and Danny Perez ([email protected]