Non-preemptive time warp scheduling algorithms

Burdorf, Christopher; Marti, Jed

doi:10.1145/382258.382787

Cited by 13 publications

(2 citation statements)

References 5 publications

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“…Runtime tuning and self optimization is a technique which has been thoroughly studied in the context of speculative simulation, focusing on diverse subsystems of traditional Time Warp runtime environments [17]. For example, it has been used to fine tune the checkpoint interval [1,13,30], to select the best checkpointing strategy [27], for event scheduling [4,24,29,31], to decide upon the technique to implement a rollback (e.g. state saving vs. reverse computation [7]), to find a proper binding between LPs and processing elements performance-wise [23,33], or to control overall memory consumption [10].…”

Section: Related Workmentioning

confidence: 99%

Autonomic Power Management in Speculative Simulation Runtime Environments

Conoci

Ianni

Marotta

et al. 2020

Proceedings of the 2020 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation

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While transitioning to exascale systems, it has become clear that power management plays a fundamental role to support a viable utilization of the underlying hardware, also performance-wise. To meet power restrictions imposed by future exascale supercomputers, runtime environments will be required to enforce self-tuning schemes to run dynamic workloads under an imposed power cap. Literature results show that, for a wide class of multi-threaded applications, tuning both the degree of parallelism and frequency/ voltage of cores allows a more effective use of the budget, compared to techniques that use only one of these mechanisms in isolation. In this paper, we explore the issues associated with applying these techniques on speculative Time-Warp based simulation runtime environments. We discuss how the differences in two antithetical Time Warp-based simulation environments impact the obtained results. Our assessment confirms that the performance gains achieved through a proper allocation of the power budget can be significant. We also identify the research challenges that would make these form of self-tuning more broadly applicable. CCS CONCEPTS• Computing methodologies → Discrete-event simulation;• Hardware → Chip-level power issues; • Software and its engineering → Software performance.

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Section: Related Workmentioning

confidence: 99%

Autonomic Power Management in Speculative Simulation Runtime Environments

Conoci

Ianni

Marotta

et al. 2020

Proceedings of the 2020 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation

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“…In our context, it can be improved upon to reduce overall PDES program execution time. It has been suggested that a better policy for scheduling simulation processes would use simulation parameters, such as LVT (logical process Local Virtual Time), or the timestamp of the next event message [8].…”

Section: Schedulingmentioning

confidence: 99%

TWLinuX: Operating System Support for Optimistic Parallel Discrete Event Simulation

Sharma¹,

Thazhuthaveetil

2001

High Performance Computing — HiPC 2001

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Abstract. Parallel or Distributed Discrete Event Simulation (PDES) refers to the concurrent execution of a single discrete event simulation application on a parallel or distributed computing system. Most available PDES implementations provide user level library support for writing distributed simulation applications. We discuss how OS support can be designed to facilitate optimistic PDES of large, complex simulation models. TWLinuX is our implementation of these concepts through modification of Linux. Through TWLinuX, a simple, low cost network of machines becomes a high performance discrete event simulation platform.

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