Distributed discrete-event simulation
ACM Computing Surveys (CSUR)
Reducing Null Messages in Misra's Distributed Discrete Event Simulation Method
IEEE Transactions on Software Engineering
Parallel and Distribution Simulation Systems
Parallel and Distribution Simulation Systems
Parallel simulation of chip-multiprocessor architectures
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Variants of the Chandy-Misra-Bryant Distributed Discrete-Event Simulation Algorithm
Variants of the Chandy-Misra-Bryant Distributed Discrete-Event Simulation Algorithm
Distributed Simulation: A Case Study in Design and Verification of Distributed Programs
IEEE Transactions on Software Engineering
Multicore Processors and Systems
Multicore Processors and Systems
Adaptive and Speculative Slack Simulations of CMPs on CMPs
MICRO '43 Proceedings of the 2010 43rd Annual IEEE/ACM International Symposium on Microarchitecture
The structural simulation toolkit
ACM SIGMETRICS Performance Evaluation Review - Special issue on the 1st international workshop on performance modeling, benchmarking and simulation of high performance computing systems (PMBS 10)
A universal parallel front-end for execution driven microarchitecture simulation
Proceedings of the 2012 Workshop on Rapid Simulation and Performance Evaluation: Methods and Tools
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This paper presents two optimization techniques for the basic Null-message algorithm in the context of parallel simulation of multicore computer architectures. Unlike the general, application-independent optimization methods, these are application-specific optimizations that make use of system properties of the simulation application. We demonstrate in two aspects that the domain-specific knowledge offers great potential for optimization. First, it allows us to send Null-messages much less eagerly, thus greatly reducing the amount of Null-messages. Second, the internal state of the simulation application allows us to make conservative forecast of future outgoing events. This leads to the creation of an enhanced synchronization algorithm called Forecast Null-message algorithm, which, by combining the forecast from both sides of a link, can greatly improve the simulation look-ahead. Compared with the basic Null-message algorithm, our optimizations greatly reduce the number of Null-messages and increase simulation performance significantly as a result. On a subset of the PARSEC benchmarks, a maximum speedup of about 6 is achieved with 17 LPs.