Sequential Performance of Asynchronous Conservative PDES Algorithms

Curry, Roger; Kiddle, Cameron; Simmonds, Rob; Unger, Brian

doi:10.1109/pads.2005.25

Cited by 14 publications

(19 citation statements)

References 13 publications

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“…There are several classic algorithms for conservative and optimistic synchronizations including asynchronous-conservative [54], Lower Bound Time Stamp (LBTS) [55] and optimistic time warp [56]. In asynchronous-conservative, there is no need to perform any global synchronization.…”

Section: Terminology and Classificationsmentioning

confidence: 99%

Manycore simulation for peta-scale system design: Motivation, tools, challenges and prospects

Zarrin

Aguiar

Barraca

2017

Simulation Modelling Practice and Theory

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link AbstractThe architecture design of peta-scale computing systems is complex and presents lots of difficulties to designs, as current tools lack support for relevant features of future scenarios. Novel systems must be designed with great care and tools, such as manycore architecture simulators, must be adapted accordingly. However, current simulation tools are very slow, often specific-purposeoriented, suffer from various issues and are rarely able to simulate thousands of cores. The emergence of peta-scale systems and the upcoming manycore era brings nevertheless new challenges to computing systems and architectures, adding further difficulties and requirements on the development of the corresponding simulators. Furthermore, the design of architecture simulators for manycore systems involve methods and techniques from various interdisciplinary research areas, which in turn brings more challenges in different aspects. As system complexity grows, the growth of the simulation capacity is being outpaced (reaching the so called simulation wall). In this paper, we present the challenges for simulating future large scale manycore environments, and we investigate the adequacy of current modeling and simulation tools, methodologies and techniques. The aim of this work is to highlight how current approaches can best deal with the identified problems, smoothing the challenges of research in future peta-scale systems.

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Section: Terminology and Classificationsmentioning

confidence: 99%

Manycore simulation for peta-scale system design: Motivation, tools, challenges and prospects

Zarrin

Aguiar

Barraca

2017

Simulation Modelling Practice and Theory

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“…Curry et al [51] studied the performance of systems using a sequential Centralized Event List (CEL) and compared the results with those of CMB. In their experiments, the performance of a CMB-based system was compared with three CEL implementations namely the heap, splay tree, and calendar queue for a particular workload model.…”

Section: Variations Of Cmbmentioning

confidence: 99%

Synchronization methods in parallel and distributed discrete-event simulation

Jafer

Liu

Wainer

2013

Simulation Modelling Practice and Theory

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a b s t r a c tThis work attempts to provide insight into the problem of executing discrete event simulation in a distributed fashion. The article serves as the state of the art in Parallel DiscreteEvent Simulation (PDES) by surveying existing algorithms and analyzing the merits and drawbacks of various techniques. We discuss the main characteristics of existing synchronization methods for parallel and distributed discrete event simulation. The two major categories of synchronization protocols, namely conservative and optimistic, are introduced and various approaches within each category are presented. We also present the latest efforts towards PDES on emerging platforms such as heterogeneous multicore processors, Web services, as well as Grid and Cloud environment.Ó 2012 Elsevier B.V. All rights reserved. Parallel discrete-event simulationWith the computing power and advanced software available today, modeling and simulation has become a cost-effective tool for detailed analysis of a broad array of natural and artificial systems. Parallel and distributed simulation is widely accepted as the technology of choice to speed up large-scale discrete-event simulation and to promote reusability and interoperability of simulation components. Parallel Discrete-Event Simulation (PDES) has received increasing interest as simulations become more time consuming and geographically distributed. A rich literature has already been developed in the last three decades, taking advantage of the increasing availability of highly parallel and distributed computing platforms. It has been several years since the last survey by Perumalla [5] and a refreshed review of the latest developments would be needed for us to ponder new research initiatives, especially on emerging platforms such as many-core processors, Internetscale simulation environments, and cloud-based virtualized infrastructures.In parallel and distributed simulations, the entire simulation task is divided into a set of smaller subtasks with each executed on a different processor or node. Hence, the simulation system is viewed as a collection of concurrent processes, each modeling a different part of the physical system and executing on a dedicated processor in a sequential fashion. These processes communicate with each other by exchanging time-stamped event messages. An event refers to an update to simulation system state at a specific simulation time instant. Throughout the simulation, events arrive at destination processes, and depending on the delivery ordering system of the simulation, they are processed differently. The two commonly used orderings are (1) receive-order and (2) timestamp-order. With the first type, events are delivered to the destination processes when they arrive at the destination. On the other hand, with the timestamp-order, events are delivered in non-decreasing order of their timestamp, requiring runtime checks and buffering to ensure such ordering.1569-190X/$ -see front matter Ó

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“…A utilização da simulação distribuída não é uma tarefa trivial, uma vez que ela se utiliza das estruturas existentes em uma simulação seqüencial e, adicionalmente, as estruturas necessárias à simulação distribuída (Fujimoto, 2000, Curry et al, 2005, Robinson, 2005 MacDougall, 1987, Fujimoto, 2000, Reed & Fujimoto, 1987, Fujimoto, 2003, Steinman, 2005, Teo & Onggo, 2004 As próximas seções discutem os protocolos de sincronização para simulação distribuída.…”

Section: Srip (Single Replication In Parallel)unclassified

“…A partir dessa época foram propostos esquemas básicos para a evolução de uma simulação distribuída. O estudo de simulação distribuída vem se tornando cada vez mais importante devido ao fator limitante do tempo computacional (Bononi et al, 2005, Curry et al, 2005. A solução seqüencial de modelos de sistemas mais complexos é mais difícil devido a grande quantidade de variáveis e à grande quantidade de dados produzidos e armazenados.…”

Section: Introductionunclassified

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"Simulação distribuída utilizando protocolos independentes e troca dinâmica nos processos lógicos"

Kawabata¹

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Sequential Performance of Asynchronous Conservative PDES Algorithms

Cited by 14 publications

References 13 publications

Manycore simulation for peta-scale system design: Motivation, tools, challenges and prospects

Manycore simulation for peta-scale system design: Motivation, tools, challenges and prospects

Synchronization methods in parallel and distributed discrete-event simulation

"Simulação distribuída utilizando protocolos independentes e troca dinâmica nos processos lógicos"

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