RUFT: Simplifying the Fat-Tree Topology

Gómez, C.; Gilabert, Francisco Jurado; Gómez, María E.; López, Pedro; Duato, J.

doi:10.1109/icpads.2008.44

Cited by 22 publications

(10 citation statements)

References 6 publications

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“…Existing work on fat-trees [31], [38], [18], [13] has not so far provided solutions for quick scaling. The key issues are to quickly distribute the newer participants among the existing 1 Such as the electron-webrtc [2] library for Node.js.…”

Section: Introductionmentioning

confidence: 99%

Genet: A Quickly Scalable Fat-Tree Overlay for Personal Volunteer Computing using WebRTC

Lavoie

Hendren

Desprez³

et al. 2019

2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)

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WebRTC enables browsers to exchange data directly but the number of possible concurrent connections to a single source is limited. We overcome the limitation by organizing participants in a fat-tree overlay: when the maximum number of connections of a tree node is reached, the new participants connect to the node's children. Our design quickly scales when a large number of participants join in a short amount of time, by relying on a novel scheme that only requires local information to route connection messages: the destination is derived from the hash value of the combined identifiers of the message's source and of the node that is holding the message. The scheme provides deterministic routing of a sequence of connection messages from a single source and probabilistic balancing of newer connections among the leaves. We show that this design puts at least 83% of nodes at the same depth as a deterministic algorithm, can connect a thousand browser windows in 21-55 seconds in a local network, and can be deployed for volunteer computing to tap into 320 cores in less than 30 seconds on a local network to increase the total throughput on the Collatz application by two orders of magnitude compared to a single core.

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Section: Introductionmentioning

confidence: 99%

Genet: A Quickly Scalable Fat-Tree Overlay for Personal Volunteer Computing using WebRTC

Lavoie

Hendren

Desprez³

et al. 2019

2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)

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“…In these works, it is shown that a proper and meticulous design process can lead to a feasible large centralized crossbar as in Figure 1.9. Similarly, in [21,24,28], it is shown that high-radix topologies are feasible without an excessive cost in terms of resources.…”

Section: Crossbarsmentioning

confidence: 99%

“…In [25], the authors show how technology constraints impose severe limitations to the practical utilization of tree-based high-radix topologies. In [24], the authors go beyond the previous simplification, and reduce the fat-tree complexity by forcing a deterministic routing algorithm, developing the Reduced Unified Fat-Tree (RUFT).…”

Section: High-performance Noc Topologiesmentioning

confidence: 99%

“…Finally, tree-based networks are feasible if some simplifications are made [24,25], creating a fat-tree topology. A fat-tree topology (see Figure 2.9) is an indirect topology where the bandwidth is doubled at each upper layer, achieving full bisection bandwidth.…”

Section: Network-on-chip Topologiesmentioning

confidence: 99%

“…In this sense, researchers have developed more complex topologies that overcome the inefficiencies of basic ones but still being implemented as a planar structure. A flattened butterfly topology [21,22] (see Figure 1.4(b)), multi-stage networks such as the Clos network [23] (see Figure 1.4(d)), topologies based on fat-tree networks [24,25] (see Figure 1.4(e)), concentrated meshes (C-meshes) [26] (see Figure 1.4(a)), hierarchical topologies [27], or multidrop express channels (MEC) [28] (see Figure 1.4(c)) are some remarkable solutions recently suggested by researchers.…”

Section: Noc Topologiesmentioning

confidence: 99%

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The k-ary n-direct s-indirect family of topologies for large-scale interconnection networks

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et al. 2016

J Supercomput

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In large-scale supercomputers, the interconnection network plays a key role in system performance. Network topology highly defines the performance and cost of the interconnection network. Direct topologies are sometimes used due to its reduced hardware cost, but the number of network dimensions is limited by the physical 3-D space, which leads to an increase of the communication latency and a reduction of network throughput for large machines. Indirect topologies can provide better performance for large machines, but at higher hardware cost. In this paper, we propose a new family of hybrid topologies, the k-ary n-direct s-indirect, that combines the best features from both direct and indirect topologies to efficiently connect an extremely high number of processing nodes. The proposed network is an n-dimensional topology where the k nodes of each dimension are connected through a small indirect topology of s stages. This combination results in a family of topologies that provides high performance, with latency and throughput figures of merit close to indirect topologies, but at a lower hardware cost. In particular, it doubles the throughput obtained per cost unit compared with indirect topologies in most of the cases. Moreover, their fault-tolerance degree is similar to the one achieved by direct topologies built with switches with the same number of ports.

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RUFT: Simplifying the Fat-Tree Topology

Cited by 22 publications

References 6 publications

Genet: A Quickly Scalable Fat-Tree Overlay for Personal Volunteer Computing using WebRTC

Genet: A Quickly Scalable Fat-Tree Overlay for Personal Volunteer Computing using WebRTC

Floorplan-Aware High Performance NoC Design

The k-ary n-direct s-indirect family of topologies for large-scale interconnection networks

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