Prediction of communication delay in torus networks under multiple time-scale correlated traffic

Min, Geyong; Ould‐Khaoua, Mohamed

doi:10.1016/j.peva.2004.10.010

Cited by 8 publications

(3 citation statements)

References 30 publications

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“…V. RELATED WORK There is a large body of previous research focusing on the modeling and simulation of torus interconnection networks. Min and OuldKhaoua [25] proposed a torus network model based on circuit switching. Here, the traffic generated on each node follows an independent stochastic process with a given arrival rate.…”

Section: Billion-node Torus Network Scaling Studymentioning

confidence: 99%

Modeling Billion-Node Torus Networks Using Massively Parallel Discrete-Event Simulation

Liu

Carothers

2011

2011 IEEE Workshop on Principles of Advanced and Distributed Simulation

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Exascale supercomputers will have millions or even hundreds of millions of processing cores and the potential for nearly billionway parallelism. Exascale compute and data storage architectures will be critically dependent on the interconnection network. The most popular interconnection network for current and future supercomputer systems is the torus (e.g., k-ary, n-cube). This paper focuses on the modeling and simulation of ultra-large-scale torus networks using Rensselaer's Optimistic Simulator System (ROSS). We compare real communication delays between our model and the actual torus network from the Blue Gene/L using 2,048 processors. Our performance experiments demonstrate the ability to simulate million to billion-node torus networks. 1 The torus network model for a 16-million-node configuration shows a high degree of strong scaling when going from 1,024 cores to 32,768 cores on Blue Gene/L with a peak event-rate of nearly 5 billion events per second. Finally, we demonstrate the performance of our torus network model configured with 1-billion-nodes using up to 16,384 Blue Gene/L processors.

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Section: Billion-node Torus Network Scaling Studymentioning

confidence: 99%

Modeling Billion-Node Torus Networks Using Massively Parallel Discrete-Event Simulation

Liu

Carothers

2011

2011 IEEE Workshop on Principles of Advanced and Distributed Simulation

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“…Additionally, Min et al [34] developed an analytic model for computing communication delays in torus networks with circuit switching under multiple time-scale bursty and correlated traffic. The probability of message blocking in practical torus topologies was calculated using the model.…”

Section: Related Workmentioning

confidence: 99%

A case study in using massively parallel simulation for extreme-scale torus network codesign

Mubarak

Carothers

Ross

et al. 2014

Proceedings of the 2nd ACM SIGSIM Conference on Principles of Advanced Discrete Simulation

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A high-bandwidth, low-latency interconnect will be a critical component of future exascale systems. The torus network topology, which uses multidimensional network links to improve path diversity and exploit locality between nodes, is a potential candidate for exascale interconnects.The communication behavior of large-scale scientific applications running on future exascale networks is particularly important and analytical/algorithmic models alone cannot deduce it. Therefore, before building systems, it is important to explore the design space and performance of candidate exascale interconnects by using simulation. We improve upon previous work in this area and present a methodology for modeling and simulating a high-fidelity, validated, and scalable torus network topology at a packet-chunk level detail using the Rensselaer Optimistic Simulation System (ROSS). We execute various configurations of a 1.3 million node torus network model in order to examine the effect of torus dimensionality on network performance with relevant HPC traffic patterns. To the best of our knowledge, these are the largest torus network simulations that are carried out at such a detailed fidelity. In terms of simulation performance, a 1.3 million node, 9-D torus network model is shown to process a simulated exascale-class workload of nearest-neighbor traffic with 100 million message injections per second per node using 65,536 Blue Gene/Q cores in a simulation run-time of only 25 seconds. We also demonstrate that massive-scale simulations are a critical tool in exascale system design since small-scale torus simulations are not always indicative of the network behavior at an exascale size. The take-away message from this case study is that massively parallel simulation is a key enabler for effective extreme-scale network codesign.

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“…Therefore, a sample ACF of traffic may be divergent if it is of infinite variance. Nonetheless, correlations of traffic play a role in practice; see, for example [12][13][14][15][16][17][18][19][20][21][22], just to cite a few. Consequently, the answer to the question whether a sample ACF of traffic is convergent or not is desired from the point of view of the theory of traffic.…”

Section: Introductionmentioning

confidence: 99%

Convergence of Sample Autocorrelation of Long-Range Dependent Traffic

Zhao

2013

Mathematical Problems in Engineering

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We depict our work on a fundamental issue in the theory of long-range dependent traffic in the aspect of the convergence of sample autocorrelation function (ACF) of real traffic. The present results suggest that the sample ACF of traffic is convergent. In addition, we show that the sample size has considerable effects in estimating the sample ACF of traffic. More precisely, a sample ACF of traffic tends to be smoother when the sample size increases.

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Prediction of communication delay in torus networks under multiple time-scale correlated traffic

Cited by 8 publications

References 30 publications

Modeling Billion-Node Torus Networks Using Massively Parallel Discrete-Event Simulation

Modeling Billion-Node Torus Networks Using Massively Parallel Discrete-Event Simulation

A case study in using massively parallel simulation for extreme-scale torus network codesign

Convergence of Sample Autocorrelation of Long-Range Dependent Traffic

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