Data-driven methods for computer simulations are blooming in many scientific areas. The traditional approach to simulating physical behaviors relies on solving partial differential equations (PDEs). ...Since calculating these iterative equations is highly both computationally demanding and time-consuming, data-driven methods leverage artificial intelligence (AI) techniques to alleviate that workload. Data-driven methods have to be trained in advance to provide their subsequent fast predictions; however, the cost of the training stage is non-negligible. This article presents a predictive model for inferencing future states of a specific fluid simulation that serves as a use case for evaluating different training alternatives. Particularly, this study compares the performance of only CPU, multi-GPU, and distributed approaches for training a time series forecasting deep learning model. With some slight code adaptations, results show and compare, in different implementations, the benefits of distributed GPU-enabled training for predicting high-accuracy states in a fraction of the time needed by the computational fluid dynamics solver.
In this study, the impact of blade radial and axial deformations on the operation safety and performance of an axial compressor is analyzed using a partitioned fluid-structure coupling approach. High ...performance computing (HPC) clusters and the Message Passage Interface (MPI) parallelization method are utilized to optimize the mesh quality and computation time balance. The chosen high-resolution tip-clearance mesh is validated through a mesh convergence study on the fluid domain. Three turbulence models (k-ε, k-ω, and k-ω SST) are compared and the k-ε turbulence model is found to be the best option for agreement with experimental data. A multilevel factorial design of experiments (DOE) is conducted to investigate the influence of tip-clearance variation on the operation safety and performance of the compressor. A parametric study for several tip-clearance values and materials is performed using ANSYS Workbench, and the maximum deformation in the blade tip was predicted to be 0.7 mm, and the optimum design point is determined based on the weight and importance of the factors, which leads to an increase over 33% in the operation safety and a negligible loss in efficiency. The study also highlights the 42 times computational time savings obtained through the use of super-processors with high quality mesh in both solid and fluid domains in comparison with a personal computer. Future work could investigate the impact of other factors such as blade geometry, or operating conditions on the performance and safety of the compressor in a two-way strongly coupled transient manner.
•The study includes an examination of different turbulence models in order to determine the most accurate one.•The fluid structure interaction (FSI) analysis has been performed to ensure the safety of the operation based on experimentally validated numerical methods.•High-performance computing clusters have been employed to reduce the computational cost and improve accuracy.•A design of experiments has been carried out employing a parametric study to evaluate the impact of different operational conditions and materials on the safety and performance of the turbomachinery to optimize the design point.
Abstract Recently, Egypt has recognized the pivotal role of High Performance Computing in advancing science and innovation. Additionally, Egypt realizes the importance of collaboration between ...different institutions and universities to consolidate their own computational and data resources into a unified platform to serve different disciplines (e.g., scientific, industrial, governmental). Otherwise, additional resources would be needed to be purchased with the associated cost, effort, and time difficulties (e.g., setup, administration, maintenance, etc.). Thus, this paper delves into the architecture and capabilities of the EN-HPCG grid using two different workload management systems: (i) Slurm (Open-Source) and (ii) PBS Pro (Licensed). This paper compares the performance of the grid between Slurm and PBS Pro in specific high-throughput computing (HTC) applications using the NAS Grid parallel benchmark (NGB) to determine which workload manager is more suitable for EN-HPCG. The evaluation includes grid-level performance metrics such as throughput, and the number of tasks completed as a function of time. Also, the presented methodology aims to assist potential partners in their decision-making process to join the EN-HPCG grid, with a focus on the site speed-up metric. Our results showed that, unless an open-source solution without cost and license problems is an obligation (in which case, Slurm is the viable solution), then it is not advisable to integrate a cluster with high-speed hardware with a cluster possessing outdated hardware when using the Slurm scheduler. In contrast, the PBS Pro scheduler takes into account online decision-making in a dynamic environment using a unified grid.
The article discusses the application of MPI technology when performing calculations on a hybrid high-performance computing cluster using virtualization on a container base. The features of the ...application of interprocess interaction in a virtualization environment associated with the construction of a single space of containers interacting over a high-performance computing network interconnect are considered. Approaches and algorithms for deploying and executing parallel processes in a computer cluster are proposed. The issue of the functioning of computing process control systems in the provision of PaaS services using virtualization technologies is considered.
The performance of scheduling algorithms for HPC jobs highly depends on the accuracy of job runtime values. Prior research has established that neither user-provided runtimes nor system-generated ...runtime predictions are accurate. We propose a new scheduling platform that performs well in spite of runtime uncertainties. The key observation that we use for building our platform is the fact that two important classes of scheduling strategies (backfilling and plan based) differ in terms of sensitivity to runtime accuracy. We first confirm this observation by performing trace-based simulations to characterize the sensitivity of different scheduling strategies to job runtime accuracy. We then apply gradient boosting tree regression as a meta-learning approach to estimate the reliability of the system-generated job runtimes. The estimated prediction reliability of job runtimes is then used to choose a specific class of scheduling algorithm. Our hybrid scheduling platform uses a plan-based scheduling strategy for jobs with high expected runtime accuracy and backfills the remaining jobs on top of the planned jobs. While resource sharing is used to minimize fragmentation of resources, a specific ratio of CPU cores is reserved for backfilling of less predictable jobs to avoid starvation of these jobs. This ratio is adapted dynamically based on the resource requirement ratio of predictable jobs among recently submitted jobs. We perform extensive trace-driven simulations on real-world production traces to show that our hybrid scheduling platform outperforms both pure backfilling and pure plan-based scheduling algorithms.
There is a tradition at our university for teaching and research in High Performance Computing (HPC) systems engineering. With exascale computing on the horizon and a shortage of HPC talent, there is ...a need for new specialists to secure the future of research computing. Whilst many institutions provide research computing training for users within their particular domain, few offer HPC engineering and infrastructure-related courses, making it difficult for students to acquire these skills. This paper outlines how and why we are training students in HPC systems engineering, including the technologies used in delivering this goal. We demonstrate the potential for a multi-tenant HPC system for education and research, using novel container and cloud-based architecture. This work is supported by our previously published work that uses the latest open-source technologies to create sustainable, fast and flexible turn-key HPC environments with secure access via an HPC portal. The proposed multi-tenant HPC resources can be deployed on a “bare metal” infrastructure or in the cloud. An evaluation of our activities over the last five years is given in terms of recruitment metrics, skills audit feedback from students, and research outputs enabled by the multi-tenant usage of the resource.
Towards high performance robotic computing Camargo-Forero, Leonardo; Royo, Pablo; Prats, Xavier
Robotics and autonomous systems,
September 2018, 2018-09-00, 2018-09-01, Volume:
107
Journal Article, Publication
Peer reviewed
Open access
Embedding a robot with a companion computer is becoming a common practice nowadays. Such computer is installed with an operatingsystem, often a Linux distribution. Moreover, Graphic Processing Units ...(GPUs) can be embedded on a robot, giving it the capacity of performing complex on-board computing tasks while executing a mission. It seems that a next logical transition, consist of deploying a cluster of computers among embedded computing cards. With this approach, a multi-robot system can be set as a High Performance Computing (HPC) cluster. The advantages of such infrastructure are many, from providing higher computing power up to setting scalable multi-robot systems. While HPC has been always seen as a speeding-up tool, we believe that HPC in the world of robotics can do much more than simply accelerating the execution of complex computing tasks. In this paper, we introduce the novel concept of High Performance Robotic Computing — HPRC, an augmentation of the ideas behind traditional HPC to fit and enhance the world of robotics. As a proof of concept, we introduce novel HPC software developed to control the motion of a set of robots using the standard parallel MPI (Message Passing Interface) library. The parallel motion software includes two operation modes: Parallel motion to specific target and swarm-like behavior. Furthermore, the HPC software is virtually scalable to control any quantity of moving robots, including Unmanned Aerial Vehicles, Unmanned Ground Vehicles, etc.
•Literature review of previous attempts of using HPC in robotic settings.•Introduction and definition of High Performance Robotic Computing (HPRC).•Results of a traditional HPC benchmark using robot companion computers.•HPRC software for the motion of multi-robot systems.
This paper introduces a novel parallel trajectory mechanism that combines Levenberg-Marquardt and Forward Accumulation Through Time algorithms to train a recurrent neural network controller in a ...closed-loop control system by distributing the calculation of trajectories across Central Processing Unit (CPU) cores/workers depending on the computing platforms, computing program languages, and software packages available. Without loss of generality, the recurrent neural network controller of a grid-connected converter for solar integration to a power system was selected as the benchmark test closed-loop control system. Two software packages were developed in Matlab and C++ to verify and demonstrate the efficiency of the proposed parallel training method. The training of the deep neural network controller was migrated from a single workstation to both cloud computing platforms and High-Performance Computing clusters. The training results show excellent speed-up performance, which significantly reduces the training time for a large number of trajectories with high sampling frequency, and further demonstrates the effectiveness and scalability of the proposed parallel mechanism.
This study uses geo-spatial crop modeling to quantify the biophysical impact of weather extremes. More specifically, the study analyzes the weather extreme which affected maize production in the USA ...in 2012; it also estimates the effect of a similar weather extreme in 2050, using future climate scenarios. The secondary impact of the weather extreme on food security in the developing world is also assessed using trend analysis.
Many studies have reported on the significant reduction in maize production in the USA due to the extreme weather event (combined heat wave and drought) that occurred in 2012. However, most of these studies focused on yield and did not assess the potential effect of weather extremes on food prices and security. The overall goal of this study was to use geo-spatial crop modeling and trend analysis to quantify the impact of weather extremes on both yield and, followed food security in the developing world.
We used historical weather data for severe extreme events that have occurred in the USA. The data were obtained from the National Climatic Data Center (NCDC) of the National Oceanic and Atmospheric Administration (NOAA). In addition we used five climate scenarios: the baseline climate which is typical of the late 20th century (2000s) and four future climate scenarios which involve a combination of two emission scenarios (A1B and B1) and two global circulation models (CSIRO-Mk3.0 and MIROC 3.2). DSSAT 4.5 was combined with GRASS GIS for geo-spatial crop modeling. Simulated maize grain yield across all affected regions in the USA indicates that average grain yield across the USA Corn Belt would decrease by 29% when the weather extremes occur using the baseline climate. If the weather extreme were to occur under the A1B emission scenario in the 2050s respectively, average grain yields would decrease by 38% and 57%, under the CSIRO-Mk3.0 and MIROC 3.2 global climate models, respectively.
The weather extremes that occurred in the USA in 2012 resulted in a sharp increase in the world maize price. In addition, it likely played a role in the reduction in world maize consumption and trade in 2012/13, compared to 2011/12. The most vulnerable countries to the weather extremes are poor countries with high maize import dependency ratios including those countries in the Caribbean, northern Africa and western Asia. Other vulnerable countries include low-income countries with low import dependency ratios but which cannot afford highly-priced maize. The study also highlighted the pathways through which a weather extreme would affect food security, were it to occur in 2050 under climate change.
Some of the policies which could help vulnerable countries counter the negative effects of weather extremes consist of social protection and safety net programs. Medium- to long-term adaptation strategies include increasing world food reserves to a level where they can be used to cover the production losses brought by weather extremes.
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