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"high‐performance computing"
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Enabling High‐Performance Cloud Computing for Earth Science Modeling on Over a Thousand Cores: Application to the GEOS‐Chem Atmospheric Chemistry Model
Cloud computing platforms can facilitate the use of Earth science models by providing immediate access to fully configured software, massive computing power, and large input data sets. However, slow internode communication performance has previously discouraged the use of cloud platforms for massively parallel simulations. Here we show that recent advances in the network performance on the Amazon Web Services cloud enable efficient model simulations with over a thousand cores. The choices of Message Passing Interface library configuration and internode communication protocol are critical to this success. Application to the Goddard Earth Observing System (GEOS)‐Chem global 3‐D chemical transport model at 50‐km horizontal resolution shows efficient scaling up to at least 1,152 cores, with performance and cost comparable to the National Aeronautics and Space Administration Pleiades supercomputing cluster.
Plain Language Summary
Earth science model simulations are computationally expensive, typically requiring the use of high‐end supercomputing clusters that are managed by universities or national laboratories. Commercial cloud computing offers an alternative. However, past work found that cloud computing platforms were not efficient for large‐scale simulations on over 100 CPU cores, because the network communication performance on the cloud was slow compared to local clusters. Here we show that recent advances in the cloud network performance enable efficient model simulations with over a thousand cores, and cloud platforms can now serve as a viable alternative to local clusters for simulations at large scale. Computing on the cloud has extensive advantages, such as providing immediate access to fully configured model code and large data sets for any users, allowing full reproducibility of model simulation results, offering quick access to novel hardware that might not be available on local clusters, and being able to scale to virtually unlimited amounts of compute and storage resources. Those benefits will help advance Earth science modeling research.
Key Points
Recent advances in network performance enable efficient model simulations with over a thousand cores on the Amazon Web Services (AWS) cloud
Performance and cost of cloud can be comparable to local supercomputing cluster
The GEOS‐Chem chemical transport model is now available and documented for massively parallel simulations on the AWS cloud
Journal Article
ModelTest-NG: A New and Scalable Tool for the Selection of DNA and Protein Evolutionary Models
ModelTest-NG is a reimplementation from scratch of jModelTest and ProtTest, two popular tools for selecting the best-fit nucleotide and amino acid substitution models, respectively. ModelTest-NG is one to two orders of magnitude faster than jModelTest and ProtTest but equally accurate and introduces several new features, such as ascertainment bias correction, mixture, and free-rate models, or the automatic processing of single partitions. ModelTest-NG is available under a GNU GPL3 license at https://github.com/ddarriba/modeltest, last accessed September 2, 2019.
Journal Article
Running applications on Oracle Exadata : tuning tips & techniques
\"Best practices for peak OLTP performance\"--Page 1 of cover.
Book
UKCropDiversity‐HPC: A collaborative high‐performance computing resource approach for sustainable agriculture and biodiversity conservation
Societal Impact Statement
Diverse gene pools are fundamental to crop improvement, biodiversity maintenance and environmental management. The UKCropDiversity‐HPC high‐performance computing resource enables seven UK institutes to perform plant and conservation research with increased efficiency, cost‐effectiveness and environmental sustainability. It supports research across numerous areas, including bioinformatics, genetics, phenomics and conservation ‐ including Artificial Intelligence approaches. Its utilisation supports many United Nations Sustainable Development Goals, including Goals‐2 (Zero Hunger), −13 (Climate Action), −15 (Life on Land), −9 (Industry, Innovation and Infrastructure) and −4 (Quality Education). Accordingly, UKCropDiversity‐HPC helps maximise the societal impact of research undertaken at our seven institutes, driving positive change for future generations.
Diverse gene pools are fundamental to crop improvement, biodiversity maintenance and environmental management. The UKCropDiversity‐HPC high‐performance computing resource enables seven UK institutes to perform plant and conservation research with increased efficiency, cost‐effectiveness and environmental sustainability. It supports research across numerous areas, including bioinformatics, genetics, phenomics and conservation ‐ including Artificial Intelligence approaches. Its utilisation supports many United Nations Sustainable Development Goals, including Goals‐2 (Zero Hunger), −13 (Climate Action), −15 (Life on Land), −9 (Industry, Innovation and Infrastructure) and −4 (Quality Education). Accordingly, UKCropDiversity‐HPC helps maximise the societal impact of research undertaken at our seven institutes, driving positive change for future generations.
Journal Article
Energy-Aware Scheduling for High-Performance Computing Systems: A Survey
High-performance computing (HPC), according to its name, is traditionally oriented toward performance, especially the execution time and scalability of the computations. However, due to the high cost and environmental issues, energy consumption has already become a very important factor that needs to be considered. The paper presents a survey of energy-aware scheduling methods used in a modern HPC environment, starting with the problem definition, tackling various goals set up for this challenge, including a bi-objective approach, power and energy constraints, and a pure energy solution, as well as metrics related to the subject. Then, considered types of HPC systems and related energy-saving mechanisms are described, from multicore-processors/graphical processing units (GPU) to more complex solutions, such as compute clusters supporting dynamic voltage and frequency scaling (DVFS), power capping, and other functionalities. The main section presents a collection of carefully selected algorithms, classified by the programming method, e.g., machine learning or fuzzy logic. Moreover, other surveys published on this subject are summarized and commented on, and finally, an overview of the current state-of-the-art with open problems and further research areas is presented.
Journal Article
N‐SDM: a high‐performance computing pipeline for Nested Species Distribution Modelling
Predicting contemporary and future species distributions is relevant for science and decision making, yet the development of high‐resolution spatial predictions for numerous taxonomic groups and regions is limited by the scalability of available modelling tools. Uniting species distribution modelling (SDM) techniques into one high‐performance computing (HPC) pipeline, we developed N‐SDM, an SDM platform aimed at delivering reproducible outputs for standard biodiversity assessments. N‐SDM was built around a spatially‐nested framework, intended at facilitating the combined use of species occurrence data retrieved from multiple sources and at various spatial scales. N‐SDM allows combining two models fitted with species and covariate data retrieved from global to regional scales, which is useful for addressing the issue of spatial niche truncation. The set of state‐of‐the‐art SDM features embodied in N‐SDM includes a newly devised covariate selection procedure, five modelling algorithms, an algorithm‐specific hyperparameter grid search, and the ensemble of small‐models approach. N‐SDM is designed to be run on HPC environments, allowing the parallel processing of thousands of species at the same time. All the information required for installing and running N‐SDM is openly available on the GitHub repository https://github.com/N‐SDM/N‐SDM.
Journal Article
On the impact of quantum computing technology on future developments in high-performance scientific computing
Quantum computing technologies have become a hot topic in academia and industry receiving much attention and financial support from all sides. Building a quantum computer that can be used practically is in itself an outstanding challenge that has become the ‘new race to the moon’. Next to researchers and vendors of future computing technologies, national authorities are showing strong interest in maturing this technology due to its known potential to break many of today’s encryption techniques, which would have significant and potentially disruptive impact on our society. It is, however, quite likely that quantum computing has beneficial impact on many computational disciplines. In this article we describe our vision of future developments in scientific computing that would be enabled by the advent of software-programmable quantum computers. We thereby assume that quantum computers will form part of a hybrid accelerated computing platform like GPUs and co-processor cards do today. In particular, we address the potential of quantum algorithms to bring major breakthroughs in applied mathematics and its applications. Finally, we give several examples that demonstrate the possible impact of quantum-accelerated scientific computing on society.
Journal Article
Predictive Simulation for Surface Fault Occurrence Using High-Performance Computing
Numerical simulations based on continuum mechanics are promising methods for the estimation of surface fault displacements. We developed a parallel finite element method program to perform such simulations and applied the program to reproduce the 2016 Kumamoto earthquake, where surface rupture was observed. We constructed an analysis model of the 5 × 5 × 1 km domain, including primary and secondary faults, and inputted the slip distribution of the primary fault, which was obtained through inversion analysis and the elastic theory of dislocation. The simulated slips on the surface were in good agreement with the observations. We then conducted a predictive simulation by inputting the slip distributions of the primary fault, which were determined using a strong ground motion prediction method for an earthquake with a specified source fault. In this simulation, no surface slip was induced in the sub-faults. A large surface slip area must be established near a sub-fault to induce the occurrence of a slip on the surface.
Journal Article