Binomial Graph: A Scalable and Fault-Tolerant Logical Network Topology

Angskun, Thara; Bosilca, George; Dongarra, Jack

doi:10.1007/978-3-540-74742-0_43

Cited by 27 publications

(28 citation statements)

References 19 publications

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“…For illustrating our framework, we have chosen to implement a library managing Binomial Graphs (BMG) [4,2] (they are known to behave well regarding Fault-Tolerance) and probabilistic Binomial Graphs (new structures that we introduce in the paper). Random structures and more precisely random algorithms[11] are known to be optimal for many problems 1 and this explains our motivation to include them in our framework.…”

Section: Motivations and Paper's Organizationmentioning

confidence: 99%

Computing Properties of Large Scalable and Fault-Tolerant Logical Networks

Cérin

Koskas

Lei³

2011

2011 23rd International Symposium on Computer Architecture and High Performance Computing

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As the number of processors embedded in high performance computing platforms becomes higher and higher, it is vital to force the developers to enhance the scalability of their codes in order to exploit all the resources of the platforms. This often requires new algorithms, techniques and methods for code development that add to the application code new properties: the presence of faults is no more an occasional event but a challenge. Scalability and Fault-Tolerance issues are also present in hidden part of any platform: the overlay network that is necessary to build for controlling the application or in the runtime system support for messaging which is also required to be scalable and fault tolerant. In this paper, we focus on the computational challenges to experiment with large scale (many millions of nodes) logical topologies. We compute Fault-Tolerant properties of different variants of Binomial Graphs (BMG) that are generated at random. For instance, we exhibit interesting properties regarding the number of links regarding some desired Fault-Tolerant properties and we compare different metrics with the Binomial Graph structure as the reference structure. A software tool has been developed for this study and we show experimental results with topologies containing 21000 nodes. We also explain the computational challenge when we deal with such large scale topologies and we introduce various probabilistic algorithms to solve the problems of computing the conventional metrics.

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Section: Motivations and Paper's Organizationmentioning

confidence: 99%

Computing Properties of Large Scalable and Fault-Tolerant Logical Networks

Cérin

Koskas

Lei³

2011

2011 23rd International Symposium on Computer Architecture and High Performance Computing

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“…To address this issue, we setup a BMG topology [4] that provides redundant communication links between agents. As a result, even if communication channels are closed because of a failure, it is possible to find another route to reach the destination, wherever the destination is.…”

Section: ) Communication Fault Tolerancementioning

confidence: 99%

“…STCI implements a few key topologies: trees, meshes, and binomial graphs (BMGs) [4]. For instance a k-ary tree is used to deploy agents across the compute nodes, providing a scalable startup.…”

Section: B Topologiesmentioning

confidence: 99%

A Runtime Environment for Supporting Research in Resilient HPC System Software &amp; Tools

Vallée

Naughton

Bohm

et al. 2013

2013 First International Symposium on Computing and Networking

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The high-performance computing (HPC) community continues to increase the size and complexity of hardware platforms that support advanced scientific workloads. The runtime environment (RTE) is a crucial layer in the software stack for these large-scale systems. The RTE manages the interface between the operating system and the application running in parallel on the machine. The deployment of applications and tools on large-scale HPC computing systems requires the RTE to manage process creation in a scalable manner, support sparse connectivity, and provide fault tolerance. We have developed a new RTE that provides a basis for building distributed execution environments and developing tools for HPC to aid research in system software and resilience. This paper describes the software architecture of the Scalable runTime Component Infrastructure (STCI), which is intended to provide a complete infrastructure for scalable start-up and management of many processes in large-scale HPC systems. We highlight features of the current implementation, which is provided as a system library that allows developers to easily use and integrate STCI in their tools and/or applications. The motivation for this work has been to support ongoing research activities in fault-tolerance for large-scale systems. We discuss the advantages of the modular framework employed and describe two use cases that demonstrate its capabilities: (i) an alternate runtime for a Message Passing Interface (MPI) stack, and (ii) a distributed control and communication substrate for a fault-injection tool.

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“…This section presents three original two-terminal routing algorithms [29]. One optimal routing algorithm is based on breadth-first search, and the two suboptimal routing algorithms are called basic and variant.…”

Section: Original Routing Algorithmsmentioning

confidence: 99%

“…Binomial graph (BMG) [29] provides desirable topological properties in terms of both scalability and faulttolerance for high performance computing such as reasonable degree, regular graph (every node has the same degree), low diameter, symmetric graph (in the sense that an average inter-nodal distance is the same from any source node) and low cost factor. It also has low message traffic density, optimal connectivity, low faultdiameter, is strongly resilient and has good optimal probability in failure cases.…”

Section: Introductionmentioning

confidence: 99%

Optimal Routing in Binomial Graph Networks

Liu

2007

Eighth International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT 2007)

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A circulant graph with n nodes and jumps j 1 , j 2 , ..., j m is a graph in which each node i, 0 ≤ i ≤ n − 1, is adjacent to all the vertices i ± j k mod n, where 1 ≤ k ≤ m. A binomial graph network (BMG) is a circulant graph where j k is the power of 2 that is less than or equal to n. This paper presents an optimal (shortest path) two-terminal routing algorithm for BMG networks. This algorithm uses only the destination address to determine the next hop in order to stay on the shortest path. Unlike the original algorithms, it does not require extra space for routing tables or additional information in the packet. The experimental results show that the new optimal algorithm is significantly faster than the original optimal algorithm.

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Binomial Graph: A Scalable and Fault-Tolerant Logical Network Topology

Cited by 27 publications

References 19 publications

Computing Properties of Large Scalable and Fault-Tolerant Logical Networks

Computing Properties of Large Scalable and Fault-Tolerant Logical Networks

A Runtime Environment for Supporting Research in Resilient HPC System Software &amp; Tools

Optimal Routing in Binomial Graph Networks

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Binomial Graph: A Scalable and Fault-Tolerant Logical Network Topology

Cited by 27 publications

References 19 publications

Computing Properties of Large Scalable and Fault-Tolerant Logical Networks

Computing Properties of Large Scalable and Fault-Tolerant Logical Networks

A Runtime Environment for Supporting Research in Resilient HPC System Software &amp;amp; Tools

Optimal Routing in Binomial Graph Networks

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Product

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A Runtime Environment for Supporting Research in Resilient HPC System Software & Tools