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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
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
Running applications on Oracle Exadata : tuning tips & techniques
\"Best practices for peak OLTP performance\"--Page 1 of cover.
Book
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
High-performance and deep pedestrian detection based on estimation of different parts
Pedestrian detection, despite the recent advances, still is of a great challenge to computer vision in wide range of diversified applications such as urban autonomous driving and intelligent transportation. Deep convolutional neural network has greatly contributed to the recent advances in pedestrian detection algorithms. The aim of this paper is to use modified single-shot detector (SSD) approach in pedestrian detection and then improve it by a novel deep architecture. The proposed deep architecture extracts initial Region of Interests (RoIs) using SSD approach, while it employs nine parallel fast RCNNs based on inception modules to estimate nine different parts of body. The proposed method takes the advantage of a secure border in each initial RoI to both create an Extended Region of Candidate Pedestrian (ERCP) and also to extract multi-RoIs. It then selects a number of RoIs within the ERCP as detected pedestrians which satisfy few reasonable criteria. We also propose a new training approach based on different body parts estimation which searches the best RoIs. Comprehensive experimental results demonstrate that the proposed method, deep model based on parts in pedestrian proposals, is a highly effective method that achieves very competitive performance on two most popular pedestrian detection datasets: Caltech-USA and INRIA. We have improved the log-average miss rate on the Caltech-USA and INRIA pedestrian datasets to 7.28% and 4.96%, respectively.
Journal Article
High-performance dataflow computing in hybrid memory systems with UPC++ DepSpawn
Dataflow computing is a very attractive paradigm for high-performance computing, given its ability to trigger computations as soon as their inputs are available. UPC++ DepSpawn is a novel task-based library that supports this model in hybrid shared/distributed memory systems on top of a Partitioned Global Address Space environment. While the initial version of the library provided good results, it suffered from a key restriction that heavily limited its performance and scalability. Namely, each process had to consider all the tasks in the application rather than only those of interest to it, an overhead that naturally grows with both the number of processes and tasks in the system. In this paper, this restriction is lifted, enabling our library to provide higher levels of performance. This way, in experiments using 768 cores the performance improved up to 40.1%, the average improvement being 16.1%.
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
Advances in Numerical Modeling for Heat Transfer and Thermal Management: A Review of Computational Approaches and Environmental Impacts
Advances in numerical modeling are essential for heat-transfer applications in electronics cooling, renewable energy, and sustainable construction. This review explores key methods like Computational Fluid Dynamics (CFD), the Finite Element Method (FEM), the Finite Volume Method (FVM), and multiphysics modeling, alongside emerging strategies such as Adaptive Mesh Refinement (AMR), machine learning (ML), reduced-order modeling (ROM), and high-performance computing (HPC). While these techniques improve accuracy and efficiency, they also increase computational energy demands, contributing to a growing carbon footprint and sustainability concerns. Sustainable computing practices, including energy-efficient algorithms and renewable-powered data centers, offer potential solutions. Additionally, the increasing energy consumption in numerical modeling highlights the need for optimization strategies to mitigate environmental impact. Future directions point to quantum computing, adaptive models, and green computing as pathways to sustainable thermal management modeling. This study systematically reviews the latest advancements in numerical heat-transfer modeling and, for the first time, provides an in-depth exploration of the roles of computational energy optimization and green computing in thermal management. This review outlines a roadmap for efficient, environmentally responsible heat-transfer models to meet evolving demands.
Journal Article