Contention-Based Nonminimal Adaptive Routing in High-Radix Networks

Fuentes, Pablo; Vallejo, Enrique; García, María Jesús Márquez; Beivide, Ramón; Rodríguez, Germán; Minkenberg, Cyriel; Valero, Mateo

doi:10.1109/ipdps.2015.78

Cited by 22 publications

(5 citation statements)

References 36 publications

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“…Different bias values were proposed based on intra-group and inter-group traffic pattern for the Dragonfly topology [10]. Thresholds were proposed to route non-minimally when the local contention and buffer occupancy exceeds a certain threshold [16]. In this work, we avoid using any threshold for any decision-making, since the optimal threshold value may vary for different applications.…”

Section: Related Workmentioning

confidence: 99%

“…q m (q nm ) : queue occupancy for min (non-minimal) path H m (H nm ) : hop count for min (non-minimal) path b : bias or constant offset 1: if H m × q m ≤ H nm × q nm + b then route non-minimally revisit the routing decision at each hop. Fuentes et al [16] proposed to route non-minimally when the local contention and buffer occupancy exceeds a static threshold but they also rely on local-only information. Sliding window was proposed to address "phantom" congestion and the non-minimal queues were averaged to better estimate the network congestion [41].…”

Section: Introductionmentioning

confidence: 99%

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Dynamic global adaptive routing in high-radix networks

Kasan

Kim

et al. 2022

Proceedings of the 49th Annual International Symposium on Computer Architecture

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Global adaptive routing is a critical component of high-radix networks in large-scale systems and is necessary to fully exploit the path diversity of a high-radix topology. The routing decision in global adaptive routing is made between minimal and non-minimal paths, often based on local information (e.g., queue occupancy) and rely on "approximate" congestion information through backpressure. Different heuristic-based adaptive routing algorithms have been proposed for high-radix topologies; however, heuristic-based routing has performance trade-off for different traffic patterns and leads to inefficient routing decisions. In addition, previously proposed global adaptive routing algorithms are static as the same routing decision algorithm is used, even if the congestion information changes. In this work, we propose a novel global adaptive routing that we refer to as dynamic global adaptive routing that adjusts the routing decision algorithm through a dynamic bias based on the network traffic and congestion to maximize performance. In particular, we propose DGB -Decoupled, Gradient descent-based Bias global adaptive routing algorithm. DGB introduces a dynamic bias to the global adaptive routing decision by leveraging gradient descent to dynamically adjust the adaptive routing bias based on the network congestion. In addition, both the local and global congestion information are decoupled in the routing decision -global information is used for the dynamic bias while local information is used in the routing decision to more accurately estimate the network congestion. Our evaluations show that DGB consistently outperforms previously proposed routing algorithms across diverse range of traffic patterns and workloads. For asymmetric traffic pattern, DGB improves throughput by 65% compared to the stateof-the-art global adaptive routing algorithm while matching the performance for symmetric traffic patterns. For trace workloads, DGB provides average performance improvement of 26%.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic global adaptive routing in high-radix networks

Kasan

Kim

et al. 2022

Proceedings of the 49th Annual International Symposium on Computer Architecture

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“…They propose a solution to overcome this problem and to avoid transient congestion, validating it by means of simulations. Other works address this problem [19], by proposing algorithms to improve congestion estimation, which are then simulated and compared to state of the art solutions. However, as also stated by the authors, approximations in the simulation are necessary because simulating the exact tiled structure of Dragonfly would be too costly.…”

Section: Related Workmentioning

confidence: 99%

Mitigating network noise on Dragonfly networks through application-aware routing

Sensi

Girolamo

Hoefler

2019

Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

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System noise can negatively impact the performance of HPC systems, and the interconnection network is one of the main factors contributing to this problem. To mitigate this effect, adaptive routing sends packets on non-minimal paths if they are less congested. However, while this may mitigate interference caused by congestion, it also generates more traffic since packets traverse additional hops, causing in turn congestion on other applications and on the application itself. In this paper, we first describe how to estimate network noise. By following these guidelines, we show how noise can be reduced by using routing algorithms which select minimal paths with a higher probability. We exploit this knowledge to design an algorithm which changes the probability of selecting minimal paths according to the application characteristics. We validate our solution on microbenchmarks and real-world applications on two systems relying on a Dragonfly interconnection network, showing noise reduction and performance improvement.is feared by datacenter operators in so-called incast or hot-spot (many-to-one) patterns. In addition to this, adaptive routing may be affected by the so-called phantom congestion problem [46]. Namely, as congestion information is propagated with some delay, a node may react too late to congestion events. In this paper, we will be first to demonstrate and quantify the influence of different routing schemes on network noise in practice.Analyzing network noise in detail is delicate because in practice, when observing application delays, it is hard to distinguish between network noise, operating system noise, and application imbalance. Other works have used network counters but may run into the fallacy that correlation is not causation by ignoring the aspects of unrelated traffic. We will clarify several potential problems in investigations of network noise and develop a set of general guidelines for our analysis. Using these guidelines, we study the relationship between different adaptive routing schemes, application performance, and network noise. Our findings on two large-scale Cray Aries systems, Piz Daint and NERSC's Cori, are remarkable: not only will changing the adaptive routing mode reduce communication times and speed up applications up to twice, but it will also significantly reduce performance variation.We find that the best routing mode that minimizes network noise and maximizes performance depends not only on the characteristics of the allocation but also on the communication load. For example, large-scale alltoall communications are best routed with the default mode whereas many other communication patterns benefit from mostly minimal routing. Because this all depends on the location of the communication peers, there is no simple static rule to select the best routing mode. To address this, we develop a simple but effective dynamic routing library that observes the network state through local network counters and adjusts the routing mode for each message based on application characteristics s...

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“…() Piggyback and the Averaging proposal in the work of Won et al consider the average occupancy (or credit count) of all the ports in a switch, somehow similar to our feedback comparison variant. Contention counters support non‐minimal routing using only local traffic demand information (the output ports that would be used under minimal routing), with early detection and almost‐immediate reaction time to traffic changes. The ECtN routing based on contention counters distributes explicit messages with port contention information (traffic demand for each global port in a Dragonfly topology), resembling ECN.…”

Section: Related Workmentioning

confidence: 99%

Non‐minimal adaptive routing based on explicit congestion notifications

Benito

Vallejo

Izu

et al. 2018

Concurrency and Computation

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Low-diameter networks require non-minimal adaptive routing to deal with varying traffic characteristics and avoid pathological performance. Such routing is based on local estimations of network congestion, based on link-level flow control credits. Dragonfly networks based on the extensions of commodity Ethernet networks using OpenFlow have been proposed for large HPC deployments with low power consumption. However, this network technology does not implement credit-based flow control. This work explores a range of routing solutions based on exploiting explicit congestion notification messages (in particular, 802.1Qau) to adapt the number of packets using non-minimal paths. The design (denoted QCN-Switch) associates a probability value to each output port. This value is updated to reflect downstream congestion and used to statistically divert traffic away from congested areas when the load is uneven, as in the case of adversarial traffic. A feedback comparison variant is designed to separate the cases of uniform traffic at saturation and adversarial traffic at low loads. Evaluation results show that QCN-Switch is a competitive design for both the uniform traffic and adversarial traffic. Furthermore, it is able to react to changes in traffic conditions in 0.4 ms or less. A sensitivity analysis identifies the best configuration and shows its performance trade-offs.

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Contention-Based Nonminimal Adaptive Routing in High-Radix Networks

Cited by 22 publications

References 36 publications

Dynamic global adaptive routing in high-radix networks

Dynamic global adaptive routing in high-radix networks

Mitigating network noise on Dragonfly networks through application-aware routing

Non‐minimal adaptive routing based on explicit congestion notifications

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