An empirical comparison of priority-queue and event-set implementations

Jones, Douglas R.

doi:10.1145/5684.5686

Cited by 178 publications

(95 citation statements)

References 12 publications

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“…From the sequential case, it is known that in some cases search tree implementations of priority queues give better performance than heap implementations [12]. In our setting of a shared-memory architecture, it turns out that a variation of a chromatic search tree, used as a priority queue, allows for a greater degree of parallelism than in previous proposals for priority queues.…”

Section: Introductionmentioning

confidence: 93%

Amortization results for chromatic search trees, with an application to priority queues

Boyar¹,

Fagerberg²,

Larsen³

1995

Lecture Notes in Computer Science

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The intention in designing data structures with relaxed balance, such as chromatic search trees, is to facilitate fast updating on shared-memory asynchronous parallel architectures. To obtain this, the updating and rebalancing have been uncoupled, so extensive locking in connection with updates is avoided. In this paper, we prove that only an amortized constant amount of rebalancing is necessary after an update in a chromatic search tree. We also prove that the amount of rebalancing done at any particular level decreases exponentially, going from the leaves toward the root. These results imply that, in principle, a linear number of processes can access the tree simultaneously. We have included one interesting application of chromatic trees. Based on these trees, a priority queue with possibilities for a greater degree of parallelism than previous proposals can be implemented. ] 1997 Academic Press

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Section: Introductionmentioning

confidence: 93%

Amortization results for chromatic search trees, with an application to priority queues

Boyar¹,

Fagerberg²,

Larsen³

1995

Lecture Notes in Computer Science

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“…Examples of list structures include the Indexed Lists [110] and the SPEEDES Queue [111]. Tree structures are exemplified by Binary Heaps, Skew Heap, and Splay Trees [112], while the Lazy Queue [113], the Ladder Queue [114], and the Calendar Queues [115] are based on multi-list structures.…”

Section: Event Set Implementationmentioning

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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“…This variability in event order means that the integrator-queue requires a fully general algorithm with characteristic log(N c ) complexity per time-step. In Neuron, both the event-and integrator-queues are based on Jones's (1986) implementation of the splay-tree algorithm of Sleator and Tarjan (1983). As we have shown, integrator-queue execution time is negligible even for simulations on the order of 10000 cells.…”

Section: Event Driven Simulationmentioning

confidence: 99%

Independent Variable Time-Step Integration of Individual Neurons for Network Simulations

Lytton

Hines

2005

Neural Computation

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Realistic neural networks involve the co-existence of stiff, coupled, continuous differential equations arising from the integrations of individual neurons, with the discrete events with delays used for modeling synaptic connections. We present here an integration method, the local variable time-step method (lvardt) that utilizes separate variable step integrators for individual neurons in the network. Cells which are undergoing excitation tend to have small time-steps and cells which are at rest with little synaptic input tend to have large time-steps. A synaptic input to a cell causes re-initialization of only that cell's integrator without affecting the integration of other cells. We illustrated the use of lvardt on three models: a worst-case synchronizing mutual-inhibition model, a best-case synfire chain model, and a more realistic thalamocortical network model.

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An empirical comparison of priority-queue and event-set implementations

Abstract: Execution times for a variety of priority-queue implementations are compared under the hold model, showing many to be faster than implicit heaps.

Cited by 178 publications

References 12 publications

Amortization results for chromatic search trees, with an application to priority queues

Amortization results for chromatic search trees, with an application to priority queues

Synchronization methods in parallel and distributed discrete-event simulation

Independent Variable Time-Step Integration of Individual Neurons for Network Simulations

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