On Automated Memoization in the Field of Simulation Parameter Studies

Stoffers, Mirko; Schemmel, Daniel; Dustmann, Oscar Soria; Wehrle, Klaus

doi:10.1145/3186316

Cited by 8 publications

(3 citation statements)

References 26 publications

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“…We also explore the memoization method [34] to further speed up the simulation. Memoization enables the simulator to cache results of selected expensive functions.…”

Section: Simulation Performance Enhancementmentioning

confidence: 99%

A Scalable Quantum Key Distribution Network Testbed Using Parallel Discrete-Event Simulation

Zhang

Chen

et al. 2022

ACM Trans. Model. Comput. Simul.

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Quantum key distribution (QKD) has been promoted as a means for secure communications. Although QKD has been widely implemented in many urban fiber networks, the large-scale deployment of QKD remains challenging. Today, researchers extensively conduct simulation-based evaluations for their designs and applications of large-scale QKD networks for cost efficiency. However, the existing discrete-event simulators offer models for QKD hardware and protocols based on sequential event execution, which limits the scale of the experiments. In this work, we explore parallel simulation of QKD networks to address this issue. Our contributions lay in the exploration of QKD network characteristics to be leveraged for parallel simulation as well as the development of a parallel simulation framework for QKD networks. We also investigate three techniques to improve the simulation performance including (1) a ladder queue based event list, (2) memoization for computationally intensive quantum state transformation information, and (3) optimization of the network partition scheme for workload balance. The experimental results show that our parallel simulator is 10 times faster than a sequential simulator when simulating a 128-node QKD network. Our linear-regression-based network partition scheme can further accelerate the simulation experiments up to two times over using a randomized network partition scheme.

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“…We also explore the memoization method [34] to further speed up the simulation. Memoization enables the simulator to cache results of selected expensive functions.…”

Section: Simulation Performance Enhancementmentioning

confidence: 99%

A Scalable Quantum Key Distribution Network Testbed Using Parallel Discrete-Event Simulation

Zhang

Chen

et al. 2022

ACM Trans. Model. Comput. Simul.

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“…Furthermore, the simulation of a quantum network involves repeated computation of this matrix multiplication. Therefore, we utilize the memoization technique [41] to…”

Section: Local Quantum State Managermentioning

confidence: 99%

Parallel Simulation of Quantum Networks with Distributed Quantum State Management

Wu¹,

Kolar²,

Chung³

et al. 2021

Preprint

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Quantum network simulators offer the opportunity to cost-efficiently investigate potential avenues to building networks that scale with the number of users, communication distance, and application demands by simulating alternative hardware designs and control protocols. Several quantum network simulators have been recently developed with these goals in mind. However, as the size of the simulated networks increases, sequential execution becomes time consuming. Parallel execution presents a suitable method for scalable simulations of large-scale quantum networks, but the unique attributes of quantum information create some unexpected challenges. In this work we identify requirements for parallel simulation of quantum networks and develop the first parallel discrete event quantum network simulator by modifying the existing serial SeQUeNCe simulator. Our contributions include the design and development of a quantum state manager (QSM) that maintains shared quantum information distributed across multiple processes. We also optimize our parallel code by minimizing the overhead of the QSM and decreasing the amount of synchronization among processes. Using these techniques, we observe a speedup of 2 to 25 times when simulating a 1,024-node linear network with 2 to 128 processes. We also observe efficiency greater than 0.5 for up to 32 processes in a linear network topology of the same size and with the same workload. We repeat this evaluation with a randomized workload on a caveman network. Finally, we also introduce several methods for partitioning networks by mapping them to different parallel simulation processes. We released the parallel SeQUeNCe simulator as an open-source tool alongside the existing sequential version.

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“…In the first article (Stoffers et al 2018), an automated memoization approach is shown to speed up simulations done as part of an overall parameter study by avoiding redundant computations. What make this approach most interesting is the support of impure functions such as C++ code blocks that perform complex pointers accesses to read object data values.…”

Section: Guest Editorial For the Tomacs Special Issue On The Principlmentioning

confidence: 99%

Guest Editorial for the TOMACS Special Issue on the Principles of Advanced Discrete Simulation (PADS)

Fujimoto¹,

Carothers²

2018

ACM Trans. Model. Comput. Simul.

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On Automated Memoization in the Field of Simulation Parameter Studies

Cited by 8 publications

References 26 publications

A Scalable Quantum Key Distribution Network Testbed Using Parallel Discrete-Event Simulation

A Scalable Quantum Key Distribution Network Testbed Using Parallel Discrete-Event Simulation

Parallel Simulation of Quantum Networks with Distributed Quantum State Management

Guest Editorial for the TOMACS Special Issue on the Principles of Advanced Discrete Simulation (PADS)

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