Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userParallel Computing Environments and Methods for Power Distribution System Simulation
66
0
0.0
(
0
)
Added by
David P. Chassin
Publication date
2004
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
The development of cost-effective highperformance parallel computing on multi-processor supercomputers makes it attractive to port excessively time consuming simulation software from personal computers (PC) to super computes. The power distribution system simulator (PDSS) takes a bottom-up approach and simulates load at the appliance level, where detailed thermal models for appliances are used. This approach works well for a small power distribution system consisting of a few thousand appliances. When the number of appliances increases, the simulation uses up the PC memory and its runtime increases to a point where the approach is no longer feasible to model a practical large power distribution system. This paper presents an effort made to port a PC-based power distribution system simulator to a 128-processor shared-memory supercomputer. The paper offers an overview of the parallel computing environment and a description of the modification made to the PDSS model. The performance of the PDSS running on a standalone PC and on the supercomputer is compared. Future research direction of utilizing parallel computing in the power distribution system simulation is also addressed.
rate research
Read More
Intelligent task placement and management of tasks in large-scale fog platforms is challenging due to the highly volatile nature of modern workload applications and sensitive user requirements of low energy consumption and response time. Container orchestration platforms have emerged to alleviate this problem with prior art either using heuristics to quickly reach scheduling decisions or AI driven methods like reinforcement learning and evolutionary approaches to adapt to dynamic scenarios. The former often fail to quickly adapt in highly dynamic environments, whereas the latter have run-times that are slow enough to negatively impact response time. Therefore, there is a need for scheduling policies that are both reactive to work efficiently in volatile environments and have low scheduling overheads. To achieve this, we propose a Gradient Based Optimization Strategy using Back-propagation of gradients with respect to Input (GOBI). Further, we leverage the accuracy of predictive digital-twin models and simulation capabilities by developing a Coupled Simulation and Container Orchestration Framework (COSCO). Using this, we create a hybrid simulation driven decision approach, GOBI*, to optimize Quality of Service (QoS) parameters. Co-simulation and the back-propagation approaches allow these methods to adapt quickly in volatile environments. Experiments conducted using real-world data on fog applications using the GOBI and GOBI* methods, show a significant improvement in terms of energy consumption, response time, Service Level Objective and scheduling time by up to 15, 40, 4, and 82 percent respectively when compared to the state-of-the-art algorithms.
A ubiquitous computing environment consists of many resources that need to be identified by users and applications. Users and developers require some way to identify resources by human readable names. In addition, ubiquitous computing environments impose additional requirements such as the ability to work well with ad hoc situations and the provision of names that depend on context. The Non-anchored Unified Naming (NUN) system was designed to satisfy these requirements. It is based on relative naming among resources and provides the ability to name arbitrary types of resources. By having resources themselves take part in naming, resources are able to able contribute their specialized knowledge into the name resolution process, making context-dependent mapping of names to resources possible. The ease of which new resource types can be added makes it simple to incorporate new types of contextual information within names. In this paper, we describe the naming system and evaluate its use.
This article demonstrates the applicability of the parallel-in-time method Parareal to the numerical solution of the Einstein gravity equations for the spherical collapse of a massless scalar field. To account for the shrinking of the spatial domain in time, a tailored load balancing scheme is proposed and compared to load balancing based on number of time steps alone. The performance of Parareal is studied for both the sub-critical and black hole case; our experiments show that Parareal generates substantial speedup and, in the super-critical regime, can reproduce Choptuiks black hole mass scaling law.
Intelligent agents offer a new and exciting way of understanding the world of work. Agent-Based Simulation (ABS), one way of using intelligent agents, carries great potential for progressing our understanding of management practices and how they link to retail performance. We have developed simulation models based on research by a multi-disciplinary team of economists, work psychologists and computer scientists. We will discuss our experiences of implementing these concepts working with a well-known retail department store. There is no doubt that management practices are linked to the performance of an organisation (Reynolds et al., 2005; Wall & Wood, 2005). Best practices have been developed, but when it comes down to the actual application of these guidelines considerable ambiguity remains regarding their effectiveness within particular contexts (Siebers et al., forthcoming a). Most Operational Research (OR) methods can only be used as analysis tools once management practices have been implemented. Often they are not very useful for giving answers to speculative what-if questions, particularly when one is interested in the development of the system over time rather than just the state of the system at a certain point in time. Simulation can be used to analyse the operation of dynamic and stochastic systems. ABS is particularly useful when complex interactions between system entities exist, such as autonomous decision making or negotiation. In an ABS model the researcher explicitly describes the decision process of simulated actors at the micro level. Structures emerge at the macro level as a result of the actions of the agents and their interactions with other agents and the environment. 3 We will show how ABS experiments can deal with testing and optimising management practices such as training, empowerment or teamwork. Hence, questions such as will staff setting their own break times improve performance? can be investigated.
The programming paradigm Map-Reduce and its main open-source implementation, Hadoop, have had an enormous impact on large scale data processing. Our goal in this expository writeup is two-fold: first, we want to present some complexity measures that allow us to talk about Map-Reduce algorithms formally, and second, we want to point out why this model is actually different from other models of parallel programming, most notably the PRAM (Parallel Random Access Memory) model. We are looking for complexity measures that are detailed enough to make fine-grained distinction between different algorithms, but which also abstract away many of the implementation details.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
