Configuration and Performance of a Beowulf Cluster for Large-Scale Scientific Simulations

2005 ◽  
Vol 7 (2) ◽  
pp. 14-26 ◽  
Author(s):  
M.K. Gobbert
Keyword(s):  
Large Scale ◽  
Beowulf Cluster ◽  
Scientific Simulations ◽  
And Performance

scholarly journals Accelerating In-Transit Co-Processing for Scientific Simulations Using Region-Based Data-Driven Analysis

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 154
Author(s):  
Marcus Walldén ◽  
Masao Okita ◽  
Fumihiko Ino ◽  
Dimitris Drikakis ◽  
Ioannis Kokkinakis
Keyword(s):  
Large Scale ◽  
Data Driven ◽  
Data Sets ◽  
Output Constraints ◽  
Data Driven Approach ◽  
Scientific Simulations ◽  
Multiple Metrics ◽  
In Transit ◽  
Multiple Compression ◽  
Large Scale Simulations

Increasing processing capabilities and input/output constraints of supercomputers have increased the use of co-processing approaches, i.e., visualizing and analyzing data sets of simulations on the fly. We present a method that evaluates the importance of different regions of simulation data and a data-driven approach that uses the proposed method to accelerate in-transit co-processing of large-scale simulations. We use the importance metrics to simultaneously employ multiple compression methods on different data regions to accelerate the in-transit co-processing. Our approach strives to adaptively compress data on the fly and uses load balancing to counteract memory imbalances. We demonstrate the method’s efficiency through a fluid mechanics application, a Richtmyer–Meshkov instability simulation, showing how to accelerate the in-transit co-processing of simulations. The results show that the proposed method expeditiously can identify regions of interest, even when using multiple metrics. Our approach achieved a speedup of 1.29× in a lossless scenario. The data decompression time was sped up by 2× compared to using a single compression method uniformly.

scholarly journals Review of Battery Management Systems (BMS) Development and Industrial Standards

Technologies ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 28
Author(s):  
Hossam A. Gabbar ◽  
Ahmed M. Othman ◽  
Muhammad R. Abdussami
Keyword(s):  
Energy Storage ◽  
Large Scale ◽  
Comprehensive Evaluation ◽  
Storage System ◽  
Energy Storage System ◽  
Battery Management System ◽  
Battery Management ◽  
Electrical Power System ◽  
Safety Aspects ◽  
And Performance

The evolving global landscape for electrical distribution and use created a need area for energy storage systems (ESS), making them among the fastest growing electrical power system products. A key element in any energy storage system is the capability to monitor, control, and optimize performance of an individual or multiple battery modules in an energy storage system and the ability to control the disconnection of the module(s) from the system in the event of abnormal conditions. This management scheme is known as “battery management system (BMS)”, which is one of the essential units in electrical equipment. BMS reacts with external events, as well with as an internal event. It is used to improve the battery performance with proper safety measures within a system. Therefore, a safe BMS is the prerequisite for operating an electrical system. This report analyzes the details of BMS for electric transportation and large-scale (stationary) energy storage. The analysis includes different aspects of BMS covering testing, component, functionalities, topology, operation, architecture, and BMS safety aspects. Additionally, current related standards and codes related to BMS are also reviewed. The report investigates BMS safety aspects, battery technology, regulation needs, and offer recommendations. It further studies current gaps in respect to the safety requirements and performance requirements of BMS by focusing mainly on the electric transportation and stationary application. The report further provides a framework for developing a new standard on BMS, especially on BMS safety and operational risk. In conclusion, four main areas of (1) BMS construction, (2) Operation Parameters, (3) BMS Integration, and (4) Installation for improvement of BMS safety and performance are identified, and detailed recommendations were provided for each area. It is recommended that a technical review of the BMS be performed for transportation electrification and large-scale (stationary) applications. A comprehensive evaluation of the components, architectures, and safety risks applicable to BMS operation is also presented.

scholarly journals Hydropower, Geothermal, and Ocean Energy

MRS Bulletin ◽  
2008 ◽  
Vol 33 (4) ◽  
pp. 389-395 ◽  
Author(s):  
Ralph E.H. Sims
Keyword(s):  
Renewable Energy ◽  
Large Scale ◽  
New Technologies ◽  
Materials Science ◽  
Development Stage ◽  
Design Development ◽  
Ocean Energy ◽  
Improve Design ◽  
And Performance ◽  
Improve Reliability

AbstractSome forms of renewable energy have long contributed to electricity generation, whereas others are just emerging. For example, large-scale hydropower is a mature technology generating about 16% of global electricity, and many smaller scale systems are also being installed worldwide. Future opportunities to improve the technology are limited but include upgrading of existing plants to gain greater performance efficiencies and reduced maintenance. Geothermal energy, widely used for power generation and direct heat applications, is also mature, but new technologies could improve plant designs, extend their lifetimes, and improve reliability. By contrast, ocean energy is an emerging renewable energy technology. Design, development, and testing of a myriad of devices remain mainly in the research and development stage, with many opportunities for materials science to improve design and performance, reduce costly maintenance procedures, and extend plant operating lifetimes under the harsh marine environment.

A Streamflow and Water Level Forecasting Model for the Ganges, Brahmaputra, and Meghna Rivers with Requisite Simplicity

2018 ◽  
Vol 19 (1) ◽  
pp. 201-225 ◽  
Author(s):  
Wahid Palash ◽  
Yudan Jiang ◽  
Ali S. Akanda ◽  
David L. Small ◽  
Amin Nozari ◽  
...  
Keyword(s):  
Water Level ◽  
Lead Time ◽  
Large Scale ◽  
Predictive Ability ◽  
Large River ◽  
Modeling Framework ◽  
Forecasting Accuracy ◽  
Weather Model ◽  
And Performance ◽  
The Ganges

A forecasting lead time of 5–10 days is desired to increase the flood response and preparedness for large river basins. Large uncertainty in observed and forecasted rainfall appears to be a key bottleneck in providing reliable flood forecasting. Significant efforts continue to be devoted to developing mechanistic hydrological models and statistical and satellite-driven methods to increase the forecasting lead time without exploring the functional utility of these complicated methods. This paper examines the utility of a data-based modeling framework with requisite simplicity that identifies key variables and processes and develops ways to track their evolution and performance. Findings suggest that models with requisite simplicity—relying on flow persistence, aggregated upstream rainfall, and travel time—can provide reliable flood forecasts comparable to relatively more complicated methods for up to 10 days lead time for the Ganges, Brahmaputra, and upper Meghna (GBM) gauging locations inside Bangladesh. Forecasting accuracy improves further by including weather-model-generated forecasted rainfall into the forecasting scheme. The use of water level in the model provides equally good forecasting accuracy for these rivers. The findings of the study also suggest that large-scale rainfall patterns captured by the satellites or weather models and their “predictive ability” of future rainfall are useful in a data-driven model to obtain skillful flood forecasts up to 10 days for the GBM basins. Ease of operationalization and reliable forecasting accuracy of the proposed framework is of particular importance for large rivers, where access to upstream gauge-measured rainfall and flow data are limited, and detailed modeling approaches are operationally prohibitive and functionally ineffective.

scholarly journals Analysis and Enhancement of IEEE 802.15.4e DSME Beacon Scheduling Model

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Kwang-il Hwang ◽  
Sung-wook Nam
Keyword(s):  
Internet Of Things ◽  
Large Scale ◽  
Ieee 802.15.4 ◽  
Design Model ◽  
Abstract Model ◽  
Modes Of Operation ◽  
Network Construction ◽  
Scheduling Model ◽  
And Performance ◽  
Ieee 802.15.4E

In order to construct a successful Internet of things (IoT), reliable network construction and maintenance in a sensor domain should be supported. However, IEEE 802.15.4, which is the most representative wireless standard for IoT, still has problems in constructing a large-scale sensor network, such as beacon collision. To overcome some problems in IEEE 802.15.4, the 15.4e task group proposed various different modes of operation. Particularly, the IEEE 802.15.4e deterministic and synchronous multichannel extension (DSME) mode presents a novel scheduling model to solve beacon collision problems. However, the DSME model specified in the 15.4e draft does not present a concrete design model but a conceptual abstract model. Therefore, in this paper we introduce a DSME beacon scheduling model and present a concrete design model. Furthermore, validity and performance of DSME are evaluated through experiments. Based on experiment results, we analyze the problems and limitations of DSME, present solutions step by step, and finally propose an enhanced DSME beacon scheduling model. Through additional experiments, we prove the performance superiority of enhanced DSME.

scholarly journals Academic resilience: underlying norms and validity of definitions

2021 ◽  
Vol 33 (1) ◽  
pp. 169-202
Author(s):  
Wangqiong Ye ◽  
Rolf Strietholt ◽  
Sigrid Blömeke
Keyword(s):  
Hong Kong ◽  
Large Scale ◽  
Developmental State ◽  
Academic Resilience ◽  
Economic Capital ◽  
Student Background ◽  
Large Scale Assessment ◽  
And Performance ◽  
Diverse Cultures ◽  
Country Specific

AbstractAcademic resilience refers to students’ capacity to perform highly despite a disadvantaged background. Although most studies using international large-scale assessment (ILSA) data defined academic resilience with two criteria, student background and achievement, their conceptualizations and operationalizations varied substantially. In a systematic review, we identified 20 ILSA studies applying different criteria, different approaches to setting thresholds (the same fixed ones across countries or relative country-specific ones), and different threshold levels. Our study on the validity of these differences and how they affected the composition of academically resilient students revealed that the classification depended heavily on the threshold applied. When a fixed background threshold was applied, the classification was likely to be affected by the developmental state of a country. This could result in an overestimation of the proportions of academically resilient students in some countries while an underestimation in others. Furthermore, compared to the application of a social or economic capital indication, applying a cultural capital indicator may lead to lower shares of disadvantaged students classified as academically resilient. The composition of academically resilient students varied significantly by gender and language depending on which indicator of human capital or which thresholds were applied reflecting underlying societal characteristics. Conclusions drawn from such different results depending on the specific conceptualizations and operationalizations would vary greatly. Finally, our study utilizing PISA 2015 data from three countries representing diverse cultures and performance levels revealed that a stronger sense of belonging to a school significantly increased the chances to be classified as academically resilient in Peru, but not in Norway or Hong Kong. In contrast, absence from school was significantly associated with academic resilience in Norway and Hong Kong, but not in Peru.

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