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9 Fundamental Concepts systems makes them very useful for teaching purpose and academic use, especially because most discrete event simulation aspects can be applied to transaction-oriented simulation and vice versa. The best-known transaction-oriented simulation language is GPSS, which stand for General Purpose Simulation System. GPSS was developed by Geoffrey Gordon at IBM around 1960 and has contributed important concepts to discrete event simulation. Later improved versions of the GPSS language were implemented in many systems, two of which are GPSS/H [36] and GPSS/PC. A detailed description of transaction-oriented simulation and the improved GPSS/H language can be found in [26]. 2.4 Parallelisation of Discrete Event Simulation Parallelisation of computer simulation is important because the growing performance of modern computer systems leads to a demand for the simulation of more and more complex systems that still result in excessive simulation time. Parallelisation reduces the time required for simulating such complex systems by performing different parts of the simulation in parallel on multiple CPUs or multiple computers within a network. There are different approaches for the parallelisation of discrete event simulation that also cover different levels of parallelisation. One approach is to perform independent simulation runs in parallel [21]. There is only little communication needed for this approach, as it is limited to sending the model and a set of parameters to each node and collecting the simulation results after the simulation runs have finished. But this approach is relatively trivial and does not reduce the simulation time of a single simulation run. It can be used for simulations that consist of many shorter simulation runs. But these simulation runs have to be independent from each other (i.e. parameters for the simulation runs do not depend on results from each other). Parameter generation Distributing parameters Simulation runs Simulation runs Simulation runs Collecting results Figure 4: Parallelisation of independent simulation runs Analysation and visualisation
10 Fundamental Concepts Two other approaches for the parallelisation of discrete event simulation are the time- parallel approach and the space-parallel approach [12]. Both can be used to reduce the simulation time of single simulation runs. The time-parallel approach partitions the simulated time into intervals [T1, T2], [T2, T3], …, [Ti, Ti+1]. Each of these time intervals is then run on separate processors or nodes. This approach relies on being able to determine the starting state of each time interval before the simulation of the earlier time interval has been completed, e.g. it has to be possible to determine the state T2 before the simulation of the time interval [T1, T2] has been completed which is only possible for certain systems to be simulated, e.g. systems with state recurrences. For the space-parallel approach the system model is partitioned into relatively independent sub-systems. Each of these sub-systems is then assigned and performed by a logical process (LP) with different LPs running on separate processors or nodes. In most cases these sub-systems will not be completely independent from each other, which is why the LPs will have to communicate with each other in order to exchange events. The space-parallel approach offers greater robustness and is applicable to most discrete event systems but the resulting speedup will depend on how the system is partitioned and how relative independent the resulting sub-systems are. A high dependency between the sub-systems will result in an increased synchronisation and communication overhead between the LPs. It will further depend on the synchronisation algorithm used. 2.5 Synchronisation Algorithms The central problem for the space-parallel simulation approach is the synchronisation of the event execution. This synchronisation is also called time management. In discrete event simulation each event has a time stamp, which is the simulated time at which the event occurs. If two events are causal dependent on each other then they have to be performed in the correct order. Because causal dependent events could originate in different LPs synchronisation between the LPs becomes very important. There are two main classes of algorithms for the event synchronisation between LPs, which are the classes of conservative and optimistic algorithms.
Page 1 and 2: TRANSACTION-ORIENTED SIMULATION IN
Page 3 and 4: Table of Content II Table of Conten
Page 5 and 6: IV Table of Content 6 VALIDATION OF
Page 7 and 8: Figures VI Figures Figure 1: Ad Hoc
Page 9 and 10: Tables VIII Tables Table 1: Change
Page 11 and 12: 2 Introduction synchronisation algo
Page 13 and 14: 2 Fundamental Concepts Fundamental
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60 Implementation The algorithm wil
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No Start Is Active Object body acti
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5.3.2 GVT Calculation and End of Si
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66 Implementation provisional simul
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Implementation relatively basic per
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70 Implementation log4j.logger.para
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proactive-log4j 72 Implementation T
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74 Implementation Memory Allocation
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76 Validation of the Parallel Simul
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6.4 Validation 4 84 Validation of t
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… LpccEnabled=true LpccClusterNum
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Validation 5.2 Validation of the Pa
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7 Conclusions 96 Conclusions Even s
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References 98 References [1] Amin K
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[19] Krafft G. Parallelisation of T
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Appendix A: Detailed GPSS Syntax 10
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Reserved word: DEPART Syntax: [] DE
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Reserved word: SEIZE Syntax: [] SEI
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Setting: LpccClusterNumber Default
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Appendix C: Simulator log4j loggers
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Logger: parallelJavaGpssSimulator.l
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114 Appendix D: Structure of the at
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Appendix E: Documentation of select
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!"#"$$%$&"'"(!))*+,-$"./#0$! 1$"))
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!/+/.7/8$ Appendix E: Documentation
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Returns: Appendix E: Documentation
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!"#$%&''$()*)")$ +',)$-)$. #,/. !"#
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!"#$%&'()#%*+,"-*.# Appendix E: Doc
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Constructor Detail !"#$%&'$(#$)*$%+
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Validation 1.1, output of simulate:
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Validation 2, output of LP: Appendi
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Appendix F: Validation output logs
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Validation 2, output of simulate: A
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Validation 3.1, output of LP1: Appe
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Validation 4, output of LP1: Append
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simulation for time 2000) (628) 20:
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Validation 4, output of simulate: A
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(135) 19:37:56,477 Total transactio
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(1295) 14:25:22,740 Simulation stop
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Validation 6.2, output of LP2: (1)
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(32) (1,2) 0 765 Block: TRANSFER 0.
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