�ݺ�ߣshows by User: gda

�ݺ�ߣshows by User: gda / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: gda / Tue, 29 Oct 2013 03:18:29 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: gda A Parallel Data Distribution Management Algorithm /slideshow/ds-rt2013gda/27681131 ds-rt-2013-gda-131029031829-phpapp01
Identifying intersections among a set of d-dimensional rectangular regions (d-rectangles) is a common problem in many simulation and modeling applications. Since algorithms for computing intersections over a large number of regions can be computationally demanding, an obvious solution is to take advantage of the multiprocessing capabilities of modern multicore processors. Unfortunately, many solutions employed for the Data Distribution Management service of the High Level Architecture are either inefficient, or can only partially be parallelized. In this paper we propose the Interval Tree Matching (ITM) algorithm for computing intersections among d-rectangles. ITM is based on a simple Interval Tree data structure, and exhibits an embarrassingly parallel structure. We implement the ITM algorithm, and compare its sequential performance with two widely used solutions (brute force and sort-based matching). We also analyze the scalability of ITM on shared-memory multicore processors. The results show that the sequential implementation of ITM is competitive with sort-based matching; moreover, the parallel implementation provides good speedup on multicore processors.]]>
Identifying intersections among a set of d-dimensional rectangular regions (d-rectangles) is a common problem in many simulation and modeling applications. Since algorithms for computing intersections over a large number of regions can be computationally demanding, an obvious solution is to take advantage of the multiprocessing capabilities of modern multicore processors. Unfortunately, many solutions employed for the Data Distribution Management service of the High Level Architecture are either inefficient, or can only partially be parallelized. In this paper we propose the Interval Tree Matching (ITM) algorithm for computing intersections among d-rectangles. ITM is based on a simple Interval Tree data structure, and exhibits an embarrassingly parallel structure. We implement the ITM algorithm, and compare its sequential performance with two widely used solutions (brute force and sort-based matching). We also analyze the scalability of ITM on shared-memory multicore processors. The results show that the sequential implementation of ITM is competitive with sort-based matching; moreover, the parallel implementation provides good speedup on multicore processors.]]> Tue, 29 Oct 2013 03:18:29 GMT /slideshow/ds-rt2013gda/27681131 gda@slideshare.net(gda) A Parallel Data Distribution Management Algorithm gda Identifying intersections among a set of d-dimensional rectangular regions (d-rectangles) is a common problem in many simulation and modeling applications. Since algorithms for computing intersections over a large number of regions can be computationally demanding, an obvious solution is to take advantage of the multiprocessing capabilities of modern multicore processors. Unfortunately, many solutions employed for the Data Distribution Management service of the High Level Architecture are either inefficient, or can only partially be parallelized. In this paper we propose the Interval Tree Matching (ITM) algorithm for computing intersections among d-rectangles. ITM is based on a simple Interval Tree data structure, and exhibits an embarrassingly parallel structure. We implement the ITM algorithm, and compare its sequential performance with two widely used solutions (brute force and sort-based matching). We also analyze the scalability of ITM on shared-memory multicore processors. The results show that the sequential implementation of ITM is competitive with sort-based matching; moreover, the parallel implementation provides good speedup on multicore processors. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ds-rt-2013-gda-131029031829-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Identifying intersections among a set of d-dimensional rectangular regions (d-rectangles) is a common problem in many simulation and modeling applications. Since algorithms for computing intersections over a large number of regions can be computationally demanding, an obvious solution is to take advantage of the multiprocessing capabilities of modern multicore processors. Unfortunately, many solutions employed for the Data Distribution Management service of the High Level Architecture are either inefficient, or can only partially be parallelized. In this paper we propose the Interval Tree Matching (ITM) algorithm for computing intersections among d-rectangles. ITM is based on a simple Interval Tree data structure, and exhibits an embarrassingly parallel structure. We implement the ITM algorithm, and compare its sequential performance with two widely used solutions (brute force and sort-based matching). We also analyze the scalability of ITM on shared-memory multicore processors. The results show that the sequential implementation of ITM is competitive with sort-based matching; moreover, the parallel implementation provides good speedup on multicore processors.

A Parallel Data Distribution Management Algorithm from Gabriele D'Angelo

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0 Time Warp on the Go /slideshow/time-warp-on-the-go/14651073 disio-ii-121009072218-phpapp02
In this paper we deal with the impact of multi and many-core processor architectures on simulation. Despite the fact that modern CPUs have an increasingly large number of cores, most softwares are still unable to take advantage of them. In the last years, many tools, programming languages and general methodologies have been proposed to help building scalable applications for multi-core architectures, but those solutions are somewhat limited. Parallel and distributed simulation is an interesting application area in which efficient and scalable multi-core implementations would be desirable. In this paper we investigate the use of the Go Programming Language to implement optimistic parallel simulations based on the Time Warp mechanism. Specifically, we describe the design, implementation and evaluation of a new parallel simulator. The scalability of the simulator is studied when in presence of a modern multi-core CPU and the effects of the Hyper-Threading technology on optimistic simulation are analyzed.]]>
In this paper we deal with the impact of multi and many-core processor architectures on simulation. Despite the fact that modern CPUs have an increasingly large number of cores, most softwares are still unable to take advantage of them. In the last years, many tools, programming languages and general methodologies have been proposed to help building scalable applications for multi-core architectures, but those solutions are somewhat limited. Parallel and distributed simulation is an interesting application area in which efficient and scalable multi-core implementations would be desirable. In this paper we investigate the use of the Go Programming Language to implement optimistic parallel simulations based on the Time Warp mechanism. Specifically, we describe the design, implementation and evaluation of a new parallel simulator. The scalability of the simulator is studied when in presence of a modern multi-core CPU and the effects of the Hyper-Threading technology on optimistic simulation are analyzed.]]> Tue, 09 Oct 2012 07:22:14 GMT /slideshow/time-warp-on-the-go/14651073 gda@slideshare.net(gda) Time Warp on the Go gda In this paper we deal with the impact of multi and many-core processor architectures on simulation. Despite the fact that modern CPUs have an increasingly large number of cores, most softwares are still unable to take advantage of them. In the last years, many tools, programming languages and general methodologies have been proposed to help building scalable applications for multi-core architectures, but those solutions are somewhat limited. Parallel and distributed simulation is an interesting application area in which efficient and scalable multi-core implementations would be desirable. In this paper we investigate the use of the Go Programming Language to implement optimistic parallel simulations based on the Time Warp mechanism. Specifically, we describe the design, implementation and evaluation of a new parallel simulator. The scalability of the simulator is studied when in presence of a modern multi-core CPU and the effects of the Hyper-Threading technology on optimistic simulation are analyzed. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/disio-ii-121009072218-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> In this paper we deal with the impact of multi and many-core processor architectures on simulation. Despite the fact that modern CPUs have an increasingly large number of cores, most softwares are still unable to take advantage of them. In the last years, many tools, programming languages and general methodologies have been proposed to help building scalable applications for multi-core architectures, but those solutions are somewhat limited. Parallel and distributed simulation is an interesting application area in which efficient and scalable multi-core implementations would be desirable. In this paper we investigate the use of the Go Programming Language to implement optimistic parallel simulations based on the Time Warp mechanism. Specifically, we describe the design, implementation and evaluation of a new parallel simulator. The scalability of the simulator is studied when in presence of a modern multi-core CPU and the effects of the Hyper-Threading technology on optimistic simulation are analyzed.

Time Warp on the Go from Gabriele D'Angelo

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0 Parallel and Distributed Simulation from Many Cores to the Public Cloud /slideshow/parallel-and-distributed-simulation-from-many-cores-to-the-public-cloud/8468203 hpcs-tutorial-2011-110630100007-phpapp02
In this tutorial paper, we will firstly review some basic simulation concepts and then introduce the parallel and distributed simulation techniques in view of some new challenges of today and tomorrow. More in particular, in the last years there has been a wide diffusion of many cores architectures and we can expect this trend to continue. On the other hand, the success of cloud computing is strongly promoting the "everything as a service'" paradigm. Is parallel and distributed simulation ready for these new challenges? The current approaches present many limitations in terms of usability and adaptivity: there is a strong need for new evaluation metrics and for revising the currently implemented mechanisms. In the last part of the paper, we propose a new approach based on multi-agent systems for the simulation of complex systems. It is possible to implement advanced techniques such as the migration of simulated entities in order to build mechanisms that are both adaptive and very easy to use. Adaptive mechanisms are able to significantly reduce the communication cost in the parallel/distributed architectures, to implement load-balance techniques and to cope with execution environments that are both variable and dynamic. Finally, such mechanisms will be used to build simulations on top of unreliable cloud services.]]>
In this tutorial paper, we will firstly review some basic simulation concepts and then introduce the parallel and distributed simulation techniques in view of some new challenges of today and tomorrow. More in particular, in the last years there has been a wide diffusion of many cores architectures and we can expect this trend to continue. On the other hand, the success of cloud computing is strongly promoting the "everything as a service'" paradigm. Is parallel and distributed simulation ready for these new challenges? The current approaches present many limitations in terms of usability and adaptivity: there is a strong need for new evaluation metrics and for revising the currently implemented mechanisms. In the last part of the paper, we propose a new approach based on multi-agent systems for the simulation of complex systems. It is possible to implement advanced techniques such as the migration of simulated entities in order to build mechanisms that are both adaptive and very easy to use. Adaptive mechanisms are able to significantly reduce the communication cost in the parallel/distributed architectures, to implement load-balance techniques and to cope with execution environments that are both variable and dynamic. Finally, such mechanisms will be used to build simulations on top of unreliable cloud services.]]> Thu, 30 Jun 2011 07:11:44 GMT /slideshow/parallel-and-distributed-simulation-from-many-cores-to-the-public-cloud/8468203 gda@slideshare.net(gda) Parallel and Distributed Simulation from Many Cores to the Public Cloud gda In this tutorial paper, we will firstly review some basic simulation concepts and then introduce the parallel and distributed simulation techniques in view of some new challenges of today and tomorrow. More in particular, in the last years there has been a wide diffusion of many cores architectures and we can expect this trend to continue. On the other hand, the success of cloud computing is strongly promoting the "everything as a service'" paradigm. Is parallel and distributed simulation ready for these new challenges? The current approaches present many limitations in terms of usability and adaptivity: there is a strong need for new evaluation metrics and for revising the currently implemented mechanisms. In the last part of the paper, we propose a new approach based on multi-agent systems for the simulation of complex systems. It is possible to implement advanced techniques such as the migration of simulated entities in order to build mechanisms that are both adaptive and very easy to use. Adaptive mechanisms are able to significantly reduce the communication cost in the parallel/distributed architectures, to implement load-balance techniques and to cope with execution environments that are both variable and dynamic. Finally, such mechanisms will be used to build simulations on top of unreliable cloud services. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/hpcs-tutorial-2011-110630100007-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> In this tutorial paper, we will firstly review some basic simulation concepts and then introduce the parallel and distributed simulation techniques in view of some new challenges of today and tomorrow. More in particular, in the last years there has been a wide diffusion of many cores architectures and we can expect this trend to continue. On the other hand, the success of cloud computing is strongly promoting the "everything as a service'" paradigm. Is parallel and distributed simulation ready for these new challenges? The current approaches present many limitations in terms of usability and adaptivity: there is a strong need for new evaluation metrics and for revising the currently implemented mechanisms. In the last part of the paper, we propose a new approach based on multi-agent systems for the simulation of complex systems. It is possible to implement advanced techniques such as the migration of simulated entities in order to build mechanisms that are both adaptive and very easy to use. Adaptive mechanisms are able to significantly reduce the communication cost in the parallel/distributed architectures, to implement load-balance techniques and to cope with execution environments that are both variable and dynamic. Finally, such mechanisms will be used to build simulations on top of unreliable cloud services.

Parallel and Distributed Simulation from Many Cores to the Public Cloud from Gabriele D'Angelo

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0 From Simulation to Online Gaming: the need for adaptive solutions /gda/from-simulation-to-online-gaming-the-need-for-adaptive-solutions simulation-to-gaming-scribd-100730023808-phpapp01
In many fields such as distributed simulation and online gaming the missing piece is adaptivity. There is a strong need for dynamic and adaptive solutions that can improve performances and react to problems.]]>
In many fields such as distributed simulation and online gaming the missing piece is adaptivity. There is a strong need for dynamic and adaptive solutions that can improve performances and react to problems.]]> Fri, 30 Jul 2010 02:37:59 GMT /gda/from-simulation-to-online-gaming-the-need-for-adaptive-solutions gda@slideshare.net(gda) From Simulation to Online Gaming: the need for adaptive solutions gda In many fields such as distributed simulation and online gaming the missing piece is adaptivity. There is a strong need for dynamic and adaptive solutions that can improve performances and react to problems. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/simulation-to-gaming-scribd-100730023808-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> In many fields such as distributed simulation and online gaming the missing piece is adaptivity. There is a strong need for dynamic and adaptive solutions that can improve performances and react to problems.

From Simulation to Online Gaming: the need for adaptive solutions from Gabriele D'Angelo

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0 Multiplayer Online Games over Scale-Free Networks: a Viable Solution? /slideshow/multiplayer-online-games-over-scalefree-networks-a-viable-solution-3475874/3475874 disio-2010-6-100319054728-phpapp02
In this paper we discuss the viability of deploying Multiplayer Online Games (MOGs) over scale-free networks.]]>
In this paper we discuss the viability of deploying Multiplayer Online Games (MOGs) over scale-free networks.]]> Fri, 19 Mar 2010 05:47:19 GMT /slideshow/multiplayer-online-games-over-scalefree-networks-a-viable-solution-3475874/3475874 gda@slideshare.net(gda) Multiplayer Online Games over Scale-Free Networks: a Viable Solution? gda In this paper we discuss the viability of deploying Multiplayer Online Games (MOGs) over scale-free networks. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/disio-2010-6-100319054728-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> In this paper we discuss the viability of deploying Multiplayer Online Games (MOGs) over scale-free networks.

Multiplayer Online Games over Scale-Free Networks: a Viable Solution? from Gabriele D'Angelo

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0 Proximity Detection in Distributed Simulation of Wireless Mobile Systems /slideshow/proximity-detection-in-distributed-simulation-of-wireless-mobile-systems/1194667 mswim-06-090325042948-phpapp01
The distributed and the Grid Computing architectures for the simulation of massively populated wireless systems have recently been considered of interest, mainly for cost reasons. Solutions for generalized proximity detection for mobile objects is a relevant problem, with a big impact on the design and the implementation of parallel and distributed simulations of wireless mobile systems. In this paper, a set of solutions based on tailored data structures, new techniques and enhancements of the existing algorithms for generalized proximity detection are proposed and analyzed, to increase the efficiency of distributed simulations. The paper includes the analysis of computation complexity of the proposed solutions and the performance evaluation of a testbed distributed simulation of ad hoc network models. Recent works have shown that the performance of distributed simulation of dynamic complex systems could benefit from a runtime migration mechanism of model entities, which reduces the communication overheads. Such migration mechanisms may interfere with the generalized proximity detection implementations. The analysis performed in this paper illustrates the effects of many possible compositions of the proposed solutions, in a real testbed simulation framework.]]>
The distributed and the Grid Computing architectures for the simulation of massively populated wireless systems have recently been considered of interest, mainly for cost reasons. Solutions for generalized proximity detection for mobile objects is a relevant problem, with a big impact on the design and the implementation of parallel and distributed simulations of wireless mobile systems. In this paper, a set of solutions based on tailored data structures, new techniques and enhancements of the existing algorithms for generalized proximity detection are proposed and analyzed, to increase the efficiency of distributed simulations. The paper includes the analysis of computation complexity of the proposed solutions and the performance evaluation of a testbed distributed simulation of ad hoc network models. Recent works have shown that the performance of distributed simulation of dynamic complex systems could benefit from a runtime migration mechanism of model entities, which reduces the communication overheads. Such migration mechanisms may interfere with the generalized proximity detection implementations. The analysis performed in this paper illustrates the effects of many possible compositions of the proposed solutions, in a real testbed simulation framework.]]> Wed, 25 Mar 2009 04:29:45 GMT /slideshow/proximity-detection-in-distributed-simulation-of-wireless-mobile-systems/1194667 gda@slideshare.net(gda) Proximity Detection in Distributed Simulation of Wireless Mobile Systems gda The distributed and the Grid Computing architectures for the simulation of massively populated wireless systems have recently been considered of interest, mainly for cost reasons. Solutions for generalized proximity detection for mobile objects is a relevant problem, with a big impact on the design and the implementation of parallel and distributed simulations of wireless mobile systems. In this paper, a set of solutions based on tailored data structures, new techniques and enhancements of the existing algorithms for generalized proximity detection are proposed and analyzed, to increase the efficiency of distributed simulations. The paper includes the analysis of computation complexity of the proposed solutions and the performance evaluation of a testbed distributed simulation of ad hoc network models. Recent works have shown that the performance of distributed simulation of dynamic complex systems could benefit from a runtime migration mechanism of model entities, which reduces the communication overheads. Such migration mechanisms may interfere with the generalized proximity detection implementations. The analysis performed in this paper illustrates the effects of many possible compositions of the proposed solutions, in a real testbed simulation framework. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/mswim-06-090325042948-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> The distributed and the Grid Computing architectures for the simulation of massively populated wireless systems have recently been considered of interest, mainly for cost reasons. Solutions for generalized proximity detection for mobile objects is a relevant problem, with a big impact on the design and the implementation of parallel and distributed simulations of wireless mobile systems. In this paper, a set of solutions based on tailored data structures, new techniques and enhancements of the existing algorithms for generalized proximity detection are proposed and analyzed, to increase the efficiency of distributed simulations. The paper includes the analysis of computation complexity of the proposed solutions and the performance evaluation of a testbed distributed simulation of ad hoc network models. Recent works have shown that the performance of distributed simulation of dynamic complex systems could benefit from a runtime migration mechanism of model entities, which reduces the communication overheads. Such migration mechanisms may interfere with the generalized proximity detection implementations. The analysis performed in this paper illustrates the effects of many possible compositions of the proposed solutions, in a real testbed simulation framework.

Proximity Detection in Distributed Simulation of Wireless Mobile Systems from Gabriele D'Angelo

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0 Simulation of Scale-Free Networks /slideshow/simulation-of-scalefree-networks-1120988/1120988 gda-simutools-2009-090309080905-phpapp01
We present a new simulation tool for scale-free networks composed of a high number of nodes. The tool, based on discrete-event simulation, enables the definition of scale-free networks composed of heterogeneous nodes and complex application-level protocols. To satisfy the performance and scalability requirements, the simulator supports both sequential (i.e. monolithic) and parallel/distributed (i.e. PADS) approaches. Furthermore, appropriate mechanisms for the communication overhead-reduction are implemented. To demonstrate the efficiency of the tool, we experiment with gossip protocols on top of scale-free networks generated by our simulator. Results of the simulations demonstrate the feasibility of our approach. The proposed tool is able to generate and manage large scale-free networks composed of thousands of nodes interacting following real-world dissemination protocols.]]>
We present a new simulation tool for scale-free networks composed of a high number of nodes. The tool, based on discrete-event simulation, enables the definition of scale-free networks composed of heterogeneous nodes and complex application-level protocols. To satisfy the performance and scalability requirements, the simulator supports both sequential (i.e. monolithic) and parallel/distributed (i.e. PADS) approaches. Furthermore, appropriate mechanisms for the communication overhead-reduction are implemented. To demonstrate the efficiency of the tool, we experiment with gossip protocols on top of scale-free networks generated by our simulator. Results of the simulations demonstrate the feasibility of our approach. The proposed tool is able to generate and manage large scale-free networks composed of thousands of nodes interacting following real-world dissemination protocols.]]> Mon, 09 Mar 2009 08:09:02 GMT /slideshow/simulation-of-scalefree-networks-1120988/1120988 gda@slideshare.net(gda) Simulation of Scale-Free Networks gda We present a new simulation tool for scale-free networks composed of a high number of nodes. The tool, based on discrete-event simulation, enables the definition of scale-free networks composed of heterogeneous nodes and complex application-level protocols. To satisfy the performance and scalability requirements, the simulator supports both sequential (i.e. monolithic) and parallel/distributed (i.e. PADS) approaches. Furthermore, appropriate mechanisms for the communication overhead-reduction are implemented. To demonstrate the efficiency of the tool, we experiment with gossip protocols on top of scale-free networks generated by our simulator. Results of the simulations demonstrate the feasibility of our approach. The proposed tool is able to generate and manage large scale-free networks composed of thousands of nodes interacting following real-world dissemination protocols. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/gda-simutools-2009-090309080905-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> We present a new simulation tool for scale-free networks composed of a high number of nodes. The tool, based on discrete-event simulation, enables the definition of scale-free networks composed of heterogeneous nodes and complex application-level protocols. To satisfy the performance and scalability requirements, the simulator supports both sequential (i.e. monolithic) and parallel/distributed (i.e. PADS) approaches. Furthermore, appropriate mechanisms for the communication overhead-reduction are implemented. To demonstrate the efficiency of the tool, we experiment with gossip protocols on top of scale-free networks generated by our simulator. Results of the simulations demonstrate the feasibility of our approach. The proposed tool is able to generate and manage large scale-free networks composed of thousands of nodes interacting following real-world dissemination protocols.

Simulation of Scale-Free Networks from Gabriele D'Angelo

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0 Parallel and Distributed Simulation of Coalition Structure Generation in Cooperative Multi-agent Systems /slideshow/parallel-and-distributed-simulation-of-coalition/502251 gdapads2008new-1215423505675315-8
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]]> Mon, 07 Jul 2008 02:41:08 GMT /slideshow/parallel-and-distributed-simulation-of-coalition/502251 gda@slideshare.net(gda) Parallel and Distributed Simulation of Coalition Structure Generation in Cooperative Multi-agent Systems gda <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/gdapads2008new-1215423505675315-8-thumbnail.jpg?width=120&height=120&fit=bounds" />

Parallel and Distributed Simulation of Coalition Structure Generation in Cooperative Multi-agent Systems from Gabriele D'Angelo

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0 Detailed Simulation of Large-Scale Wireless Networks /slideshow/detailed-simulation-of-wireless-networks/198441 detailed-simulation-of-wireless-networks-1197302494627127-3
WiFra is a new framework for the detailed simulation of very large-scale wireless networks. It is based on the parallel and distributed simulation approach and provides high scalability in terms of size of simulated networks and number of execution units running the simulation. In order to improve the performance of distributed simulation, additional techniques are proposed. Their aim is to reduce the communication overhead and to maintain a good level of load-balancing. Simulation architectures composed of low-cost Commercial-Off-The-Shelf (COTS) hardware are specifically supported by WiFra. The framework dynamically reconfigures the simulation, taking care of the performance of each part of the execution architecture and dealing with unpredictable fluctuations of the available computation power and communication load on the single execution units. A fine-grained model of the 802.11 DCF protocol has been used for the performance evaluation of the proposed framework. The results demonstrate that the distributed approach is suitable for the detailed simulation of very-large scale wireless networks.]]>
WiFra is a new framework for the detailed simulation of very large-scale wireless networks. It is based on the parallel and distributed simulation approach and provides high scalability in terms of size of simulated networks and number of execution units running the simulation. In order to improve the performance of distributed simulation, additional techniques are proposed. Their aim is to reduce the communication overhead and to maintain a good level of load-balancing. Simulation architectures composed of low-cost Commercial-Off-The-Shelf (COTS) hardware are specifically supported by WiFra. The framework dynamically reconfigures the simulation, taking care of the performance of each part of the execution architecture and dealing with unpredictable fluctuations of the available computation power and communication load on the single execution units. A fine-grained model of the 802.11 DCF protocol has been used for the performance evaluation of the proposed framework. The results demonstrate that the distributed approach is suitable for the detailed simulation of very-large scale wireless networks.]]> Mon, 10 Dec 2007 08:01:35 GMT /slideshow/detailed-simulation-of-wireless-networks/198441 gda@slideshare.net(gda) Detailed Simulation of Large-Scale Wireless Networks gda WiFra is a new framework for the detailed simulation of very large-scale wireless networks. It is based on the parallel and distributed simulation approach and provides high scalability in terms of size of simulated networks and number of execution units running the simulation. In order to improve the performance of distributed simulation, additional techniques are proposed. Their aim is to reduce the communication overhead and to maintain a good level of load-balancing. Simulation architectures composed of low-cost Commercial-Off-The-Shelf (COTS) hardware are specifically supported by WiFra. The framework dynamically reconfigures the simulation, taking care of the performance of each part of the execution architecture and dealing with unpredictable fluctuations of the available computation power and communication load on the single execution units. A fine-grained model of the 802.11 DCF protocol has been used for the performance evaluation of the proposed framework. The results demonstrate that the distributed approach is suitable for the detailed simulation of very-large scale wireless networks. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/detailed-simulation-of-wireless-networks-1197302494627127-3-thumbnail.jpg?width=120&height=120&fit=bounds" /> WiFra is a new framework for the detailed simulation of very large-scale wireless networks. It is based on the parallel and distributed simulation approach and provides high scalability in terms of size of simulated networks and number of execution units running the simulation. In order to improve the performance of distributed simulation, additional techniques are proposed. Their aim is to reduce the communication overhead and to maintain a good level of load-balancing. Simulation architectures composed of low-cost Commercial-Off-The-Shelf (COTS) hardware are specifically supported by WiFra. The framework dynamically reconfigures the simulation, taking care of the performance of each part of the execution architecture and dealing with unpredictable fluctuations of the available computation power and communication load on the single execution units. A fine-grained model of the 802.11 DCF protocol has been used for the performance evaluation of the proposed framework. The results demonstrate that the distributed approach is suitable for the detailed simulation of very-large scale wireless networks.

Detailed Simulation of Large-Scale Wireless Networks from Gabriele D'Angelo

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0 An Adaptive Load Balancing Middleware for Distributed Simulation /slideshow/an-adaptive-load-balancing-middleware-for-distributed-simulation/17410 an-adaptive-load-balancing-middleware-for-distributed-simulation-11779
The simulation is useful to support the design and performance evaluation of complex systems, possibly composed by a massive number of interacting entities. For this reason, the simulation of such systems may need aggregate computation and memory resources obtained by clusters of parallel and distributed execution units. Shared computer clusters composed of available Commercial-Off-the-Shelf hardware are preferable to dedicated systems, mainly for cost reasons. The performance of distributed simulations is influenced by the heterogeneity of execution units and by their respective CPU load in background. Adaptive load balancing mechanisms could improve the resources utilization and the simulation process execution, by dynamically tuning the simulation load with an eye to the synchronization and communication overheads reduction. In this work it will be presented the GAIA+ framework: a new load balancing mechanism for distributed simulation. The framework has been evaluated by performing testbed simulations of a wireless ad hoc network model. Results confirm the effectiveness of the proposed solutions.]]>
The simulation is useful to support the design and performance evaluation of complex systems, possibly composed by a massive number of interacting entities. For this reason, the simulation of such systems may need aggregate computation and memory resources obtained by clusters of parallel and distributed execution units. Shared computer clusters composed of available Commercial-Off-the-Shelf hardware are preferable to dedicated systems, mainly for cost reasons. The performance of distributed simulations is influenced by the heterogeneity of execution units and by their respective CPU load in background. Adaptive load balancing mechanisms could improve the resources utilization and the simulation process execution, by dynamically tuning the simulation load with an eye to the synchronization and communication overheads reduction. In this work it will be presented the GAIA+ framework: a new load balancing mechanism for distributed simulation. The framework has been evaluated by performing testbed simulations of a wireless ad hoc network model. Results confirm the effectiveness of the proposed solutions.]]> Fri, 05 Jan 2007 07:42:06 GMT /slideshow/an-adaptive-load-balancing-middleware-for-distributed-simulation/17410 gda@slideshare.net(gda) An Adaptive Load Balancing Middleware for Distributed Simulation gda The simulation is useful to support the design and performance evaluation of complex systems, possibly composed by a massive number of interacting entities. For this reason, the simulation of such systems may need aggregate computation and memory resources obtained by clusters of parallel and distributed execution units. Shared computer clusters composed of available Commercial-Off-the-Shelf hardware are preferable to dedicated systems, mainly for cost reasons. The performance of distributed simulations is influenced by the heterogeneity of execution units and by their respective CPU load in background. Adaptive load balancing mechanisms could improve the resources utilization and the simulation process execution, by dynamically tuning the simulation load with an eye to the synchronization and communication overheads reduction. In this work it will be presented the GAIA+ framework: a new load balancing mechanism for distributed simulation. The framework has been evaluated by performing testbed simulations of a wireless ad hoc network model. Results confirm the effectiveness of the proposed solutions. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/an-adaptive-load-balancing-middleware-for-distributed-simulation-11779-thumbnail.jpg?width=120&height=120&fit=bounds" /> The simulation is useful to support the design and performance evaluation of complex systems, possibly composed by a massive number of interacting entities. For this reason, the simulation of such systems may need aggregate computation and memory resources obtained by clusters of parallel and distributed execution units. Shared computer clusters composed of available Commercial-Off-the-Shelf hardware are preferable to dedicated systems, mainly for cost reasons. The performance of distributed simulations is influenced by the heterogeneity of execution units and by their respective CPU load in background. Adaptive load balancing mechanisms could improve the resources utilization and the simulation process execution, by dynamically tuning the simulation load with an eye to the synchronization and communication overheads reduction. In this work it will be presented the GAIA+ framework: a new load balancing mechanism for distributed simulation. The framework has been evaluated by performing testbed simulations of a wireless ad hoc network model. Results confirm the effectiveness of the proposed solutions.

An Adaptive Load Balancing Middleware for Distributed Simulation from Gabriele D'Angelo

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0 Concurrent Replication of Parallel and Distributed Simulations /slideshow/concurrent-replication-of-parallel-and-distributed-simulations/4755 concurrent-replication-of-parallel-and-distributed-simulations-22053
Parallel and distributed simulations enable the analysis of complex systems by concurrently exploiting the aggregate computation power and memory of clusters of execution units. In this work we investigate a new direction for increasing both the speedup of a simulation process and the utilization of computation and communication resources. Many simulation-based investigations require to collect independent observations for a correct and significant statistical analysis of results. The execution of many independent parallel or distributed simulation runs may suffer the speedup reduction due to rollbacks under the optimistic approach, and due to idle CPU times originated by synchronization and communication bottlenecks under the conservative approach. We present a parallel and distributed simulation framework supporting concurrent replication of parallel and distributed simulations (CR-PADS), as an alternative to the execution of a linear sequence of multiple parallel or distributed simulation runs. Results obtained from tests executed under variable scenarios show that speedup and resource utilization gains could be obtained by adopting the proposed replication approach in addition to the pure parallel and distributed simulation. ]]>
Parallel and distributed simulations enable the analysis of complex systems by concurrently exploiting the aggregate computation power and memory of clusters of execution units. In this work we investigate a new direction for increasing both the speedup of a simulation process and the utilization of computation and communication resources. Many simulation-based investigations require to collect independent observations for a correct and significant statistical analysis of results. The execution of many independent parallel or distributed simulation runs may suffer the speedup reduction due to rollbacks under the optimistic approach, and due to idle CPU times originated by synchronization and communication bottlenecks under the conservative approach. We present a parallel and distributed simulation framework supporting concurrent replication of parallel and distributed simulations (CR-PADS), as an alternative to the execution of a linear sequence of multiple parallel or distributed simulation runs. Results obtained from tests executed under variable scenarios show that speedup and resource utilization gains could be obtained by adopting the proposed replication approach in addition to the pure parallel and distributed simulation. ]]> Sun, 08 Oct 2006 11:32:41 GMT /slideshow/concurrent-replication-of-parallel-and-distributed-simulations/4755 gda@slideshare.net(gda) Concurrent Replication of Parallel and Distributed Simulations gda Parallel and distributed simulations enable the analysis of complex systems by concurrently exploiting the aggregate computation power and memory of clusters of execution units. In this work we investigate a new direction for increasing both the speedup of a simulation process and the utilization of computation and communication resources. Many simulation-based investigations require to collect independent observations for a correct and significant statistical analysis of results. The execution of many independent parallel or distributed simulation runs may suffer the speedup reduction due to rollbacks under the optimistic approach, and due to idle CPU times originated by synchronization and communication bottlenecks under the conservative approach. We present a parallel and distributed simulation framework supporting concurrent replication of parallel and distributed simulations (CR-PADS), as an alternative to the execution of a linear sequence of multiple parallel or distributed simulation runs. Results obtained from tests executed under variable scenarios show that speedup and resource utilization gains could be obtained by adopting the proposed replication approach in addition to the pure parallel and distributed simulation. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/concurrent-replication-of-parallel-and-distributed-simulations-22053-thumbnail.jpg?width=120&height=120&fit=bounds" /> Parallel and distributed simulations enable the analysis of complex systems by concurrently exploiting the aggregate computation power and memory of clusters of execution units. In this work we investigate a new direction for increasing both the speedup of a simulation process and the utilization of computation and communication resources. Many simulation-based investigations require to collect independent observations for a correct and significant statistical analysis of results. The execution of many independent parallel or distributed simulation runs may suffer the speedup reduction due to rollbacks under the optimistic approach, and due to idle CPU times originated by synchronization and communication bottlenecks under the conservative approach. We present a parallel and distributed simulation framework supporting concurrent replication of parallel and distributed simulations (CR-PADS), as an alternative to the execution of a linear sequence of multiple parallel or distributed simulation runs. Results obtained from tests executed under variable scenarios show that speedup and resource utilization gains could be obtained by adopting the proposed replication approach in addition to the pure parallel and distributed simulation.

Concurrent Replication of Parallel and Distributed Simulations from Gabriele D'Angelo

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0 https://cdn.slidesharecdn.com/profile-photo-gda-48x48.jpg?cb=1528895137 [For every complex problem, there is an answer that is short, simple and wrong] H. L. Mencken www.cs.unibo.it/~gdangelo/ https://cdn.slidesharecdn.com/ss_thumbnails/ds-rt-2013-gda-131029031829-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/ds-rt2013gda/27681131 A Parallel Data Distri... https://cdn.slidesharecdn.com/ss_thumbnails/disio-ii-121009072218-phpapp02-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/time-warp-on-the-go/14651073 Time Warp on the Go https://cdn.slidesharecdn.com/ss_thumbnails/hpcs-tutorial-2011-110630100007-phpapp02-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/parallel-and-distributed-simulation-from-many-cores-to-the-public-cloud/8468203 Parallel and Distribut...