Unsupervised machine learning algorithms as support tools in molecular dynamics simulations

Unsupervised Machine Learning algorithms such as clustering offer convenient features for data analysis tasks. When combined with other tools like visualization software, the possibilities of automated analysis may be greatly enhanced. In the context of Molecular Dynamics simulations, in particular asymmetric granular collisions which typically consist of thousands of particles, it is key to distinguish the fragments in which the system is divided after a collision for classification purposes. In this work we explore the unsupervised Machine Learning algorithms k-means and AGNES to distinguish groups of particles in molecular dynamics simulations, with encouraging results according to performance metrics such as accuracy and precision. We also report computational times for each algorithm, where k-means results faster than AGNES. Finally, we delineate the integration of these type of algorithms with a well-known analysis and visualization tool widely used in the physics community.Sociedad Argentina de Informática e Investigación Operativ

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

Unsupervised machine learning algorithms as support tools in molecular dynamics simulations

Unsupervised Machine Learning algorithms such as clustering offer convenient features for data analysis tasks. When combined with other tools like visualization software, the possibilities of automated analysis may be greatly enhanced. In the context of Molecular Dynamics simulations, in particular asymmetric granular collisions which typically consist of thousands of particles, it is key to distinguish the fragments in which the system is divided after a collision for classification purposes. In this work we explore the unsupervised Machine Learning algorithms k-means and AGNES to distinguish groups of particles in molecular dynamics simulations, with encouraging results according to performance metrics such as accuracy and precision. We also report computational times for each algorithm, where k-means results faster than AGNES. Finally, we delineate the integration of these type of algorithms with a well-known analysis and visualization tool widely used in the physics community.Sociedad Argentina de Informática e Investigación Operativ

Parallel execution of a parameter sweep for molecular dynamics simulations in a hybrid GPU/CPU environment

Molecular Dynamics (MD) simulations can help to utimingnderstand an immense number of phenomena at the nano and microscale. They often require the exploration of large parameter space, and a possible parallelization strategy consists of sending different parameter sets to different processors. Here we present such approach using a hybrid environment of Graphic Processing Units (GPUs) and CPU cores. We take advantage of the software LAMMPS (lammps.sandia.gov), which is already prepared to run in a hybrid environment, in order to do an efficient parameter sweep. One example is presented in this work: the collision of two clusters is sampled over a multivariate space to obtain information on the resulting structural properties.Eje: Workshop Procesamiento distribuido y paralelo (WPDP)Red de Universidades con Carreras en Informática (RedUNCI

Parallel implementation of a cellular automata in a hybrid CPU/GPU environment

Cellular Automata (CA) simulations can be used to model multiple systems, in fields like biology, physics and mathematics. In this work, a possible framework to execute a popular CA in hybrid CPU and GPUs (Graphics Processing Units) environments is presented. The inherently parallel nature of CA and the parallelism offered by GPUs makes their combination attractive. Benchmarks are conducted in several hardware scenarios. The use of MPI /OMP is explored for CPUs, together with the use of MPI in GPU clusters. Speed-ups up to 20 x are found when comparing GPU implementations to the serial CPU version of the code.WPDP- XIII Workshop procesamiento distribuido y paraleloRed de Universidades con Carreras en Informática (RedUNCI

Mortality from gastrointestinal congenital anomalies at 264 hospitals in 74 low-income, middle-income, and high-income countries: a multicentre, international, prospective cohort study

Summary Background Congenital anomalies are the fifth leading cause of mortality in children younger than 5 years globally. Many gastrointestinal congenital anomalies are fatal without timely access to neonatal surgical care, but few studies have been done on these conditions in low-income and middle-income countries (LMICs). We compared outcomes of the seven most common gastrointestinal congenital anomalies in low-income, middle-income, and high-income countries globally, and identified factors associated with mortality. Methods We did a multicentre, international prospective cohort study of patients younger than 16 years, presenting to hospital for the first time with oesophageal atresia, congenital diaphragmatic hernia, intestinal atresia, gastroschisis, exomphalos, anorectal malformation, and Hirschsprung’s disease. Recruitment was of consecutive patients for a minimum of 1 month between October, 2018, and April, 2019. We collected data on patient demographics, clinical status, interventions, and outcomes using the REDCap platform. Patients were followed up for 30 days after primary intervention, or 30 days after admission if they did not receive an intervention. The primary outcome was all-cause, in-hospital mortality for all conditions combined and each condition individually, stratified by country income status. We did a complete case analysis. Findings We included 3849 patients with 3975 study conditions (560 with oesophageal atresia, 448 with congenital diaphragmatic hernia, 681 with intestinal atresia, 453 with gastroschisis, 325 with exomphalos, 991 with anorectal malformation, and 517 with Hirschsprung’s disease) from 264 hospitals (89 in high-income countries, 166 in middleincome countries, and nine in low-income countries) in 74 countries. Of the 3849 patients, 2231 (58·0%) were male. Median gestational age at birth was 38 weeks (IQR 36–39) and median bodyweight at presentation was 2·8 kg (2·3–3·3). Mortality among all patients was 37 (39·8%) of 93 in low-income countries, 583 (20·4%) of 2860 in middle-income countries, and 50 (5·6%) of 896 in high-income countries (p<0·0001 between all country income groups). Gastroschisis had the greatest difference in mortality between country income strata (nine [90·0%] of ten in lowincome countries, 97 [31·9%] of 304 in middle-income countries, and two [1·4%] of 139 in high-income countries; p≤0·0001 between all country income groups). Factors significantly associated with higher mortality for all patients combined included country income status (low-income vs high-income countries, risk ratio 2·78 [95% CI 1·88–4·11], p<0·0001; middle-income vs high-income countries, 2·11 [1·59–2·79], p<0·0001), sepsis at presentation (1·20 [1·04–1·40], p=0·016), higher American Society of Anesthesiologists (ASA) score at primary intervention (ASA 4–5 vs ASA 1–2, 1·82 [1·40–2·35], p<0·0001; ASA 3 vs ASA 1–2, 1·58, [1·30–1·92], p<0·0001]), surgical safety checklist not used (1·39 [1·02–1·90], p=0·035), and ventilation or parenteral nutrition unavailable when needed (ventilation 1·96, [1·41–2·71], p=0·0001; parenteral nutrition 1·35, [1·05–1·74], p=0·018). Administration of parenteral nutrition (0·61, [0·47–0·79], p=0·0002) and use of a peripherally inserted central catheter (0·65 [0·50–0·86], p=0·0024) or percutaneous central line (0·69 [0·48–1·00], p=0·049) were associated with lower mortality. Interpretation Unacceptable differences in mortality exist for gastrointestinal congenital anomalies between lowincome, middle-income, and high-income countries. Improving access to quality neonatal surgical care in LMICs will be vital to achieve Sustainable Development Goal 3.2 of ending preventable deaths in neonates and children younger than 5 years by 2030