TopGen: A Library to Provide Simulation Tools with the Modeling of Interconnection Network Topologies

Topology modeling is a challenging topic in the design and development of tools that simulate the behavior of interconnection networks. During the last years we have seen the birth of several simulation tools and frameworks modeling interconnects, some of them doing brilliant efforts to do the code re-usable and extensible. The main differences among these simulation tools and frameworks is the level of granularity and abstraction of their network models. Different network models may generate duplicated efforts in modeling certain aspects of them, such as the network topology or the routing algorithm. If the topology generation and routing algorithm modeling were separated from the modeling of other network aspects, then the simulator developers could focus on developing non-existing models and new functionality, without wasting time in doing a work previously done by other simulation tool developers. In this paper we describe TopGen, an external library that can be integrated with any tool that simulates interconnection networks. TopGen provides a compendious of well-known network topologies that can be used to interconnect the network components, such as end nodes, channels, switches and routers. It also provides the corresponding knowledge to apply a compendious of routing algorithms to the modeled network topologies. TopGen also supports the definition of customized topologies that are not the standard ones, thanks to a special interface used for this purpose. We also provide details on how to integrate TopGen with a network simulator, by means of a simple API. Finally, we describe several use cases of the TopGen library.

[1]  Nan Jiang,et al.  A detailed and flexible cycle-accurate Network-on-Chip simulator , 2013, 2013 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS).

[2]  Nan Jiang,et al.  Indirect adaptive routing on large scale interconnection networks , 2009, ISCA '09.

[3]  Torsten Hoefler,et al.  Slim Fly: A Cost Effective Low-Diameter Network Topology , 2014, SC14: International Conference for High Performance Computing, Networking, Storage and Analysis.

[4]  José Duato,et al.  An Effective and Feasible Congestion Management Technique for High-Performance MINs with Tag-Based Distributed Routing , 2013, IEEE Transactions on Parallel and Distributed Systems.

[5]  Pedro López,et al.  The k-ary n-direct s-indirect family of topologies for large-scale interconnection networks , 2016, The Journal of Supercomputing.

[6]  Mohan Kumar,et al.  On generalized fat trees , 1995, Proceedings of 9th International Parallel Processing Symposium.

[7]  Henri Casanova,et al.  Versatile, scalable, and accurate simulation of distributed applications and platforms , 2014, J. Parallel Distributed Comput..

[8]  Sudhakar Yalamanchili,et al.  Interconnection Networks: An Engineering Approach , 2002 .

[9]  Jung Ho Ahn,et al.  HyperX: topology, routing, and packaging of efficient large-scale networks , 2009, Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis.

[10]  José Duato,et al.  Formalization and configuration methodology for high-radix combined switches , 2014, The Journal of Supercomputing.

[11]  Klaus Wehrle,et al.  Modeling and Tools for Network Simulation , 2010, Modeling and Tools for Network Simulation.

[12]  William J. Dally,et al.  Flattened butterfly: a cost-efficient topology for high-radix networks , 2007, ISCA '07.

[13]  Eitan Zahavi,et al.  Fat-Trees Routing and Node Ordering Providing Contention Free Traffic for MPI Global Collectives , 2011, 2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum.

[14]  Ramón Beivide,et al.  Random Folded Clos Topologies for Datacenter Networks , 2017, 2017 IEEE International Symposium on High Performance Computer Architecture (HPCA).

[15]  Torsten Hoefler,et al.  Adaptive Routing Strategies for Modern High Performance Networks , 2008, 2008 16th IEEE Symposium on High Performance Interconnects.

[16]  Darren J. Kerbyson,et al.  Optimized InfiniBand TM fat-tree routing for shift all-to-all communication patterns , 2010, ISC 2010.

[17]  Francisco J. Quiles,et al.  On the Impact of Routing Algorithms in the Effectiveness of Queuing Schemes in High-Performance Interconnection Networks , 2017, 2017 IEEE 25th Annual Symposium on High-Performance Interconnects (HOTI).

[18]  Mike Higgins,et al.  Cray Cascade: A scalable HPC system based on a Dragonfly network , 2012, 2012 International Conference for High Performance Computing, Networking, Storage and Analysis.

[19]  William J. Dally,et al.  Adaptive Routing in High-Radix Clos Network , 2006, ACM/IEEE SC 2006 Conference (SC'06).

[20]  Bruce Jacob,et al.  The structural simulation toolkit , 2006, PERV.

[21]  Fabrizio Petrini,et al.  k-ary n-trees: high performance networks for massively parallel architectures , 1997, Proceedings 11th International Parallel Processing Symposium.

[22]  Avinoam Kolodny,et al.  Distributed Adaptive Routing Convergence to Non-Blocking DCN Routing Assignments , 2014, IEEE Journal on Selected Areas in Communications.

[23]  Eitan Zahavi,et al.  Scalable Deadlock-Free Deterministic Minimal-Path Routing Engine for InfiniBand-Based Dragonfly Networks , 2018, IEEE Transactions on Parallel and Distributed Systems.

[24]  Francisco J. Quiles,et al.  Combining OpenFabrics Software and Simulation Tools for Modeling InfiniBand-Based Interconnection Networks , 2016, 2016 2nd IEEE International Workshop on High-Performance Interconnection Networks in the Exascale and Big-Data Era (HiPINEB).

[25]  William J. Dally,et al.  Technology-Driven, Highly-Scalable Dragonfly Topology , 2008, 2008 International Symposium on Computer Architecture.

[26]  Valentin Puente,et al.  TOPAZ: An Open-Source Interconnection Network Simulator for Chip Multiprocessors and Supercomputers , 2012, 2012 IEEE/ACM Sixth International Symposium on Networks-on-Chip.

[27]  Xin Yuan,et al.  Oblivious routing in fat-tree based system area networks with uncertain traffic demands , 2009, TNET.

[28]  Charles E. Leiserson,et al.  Fat-trees: Universal networks for hardware-efficient supercomputing , 1985, IEEE Transactions on Computers.

[29]  Francisco J. Quiles,et al.  Towards Modeling Interconnection Networks of Exascale Systems with OMNet++ , 2013, 2013 21st Euromicro International Conference on Parallel, Distributed, and Network-Based Processing.

[30]  Haitao Wu,et al.  BCube: a high performance, server-centric network architecture for modular data centers , 2009, SIGCOMM '09.

[31]  Michael Lang,et al.  Optimized InfiniBandTM fat‐tree routing for shift all‐to‐all communication patterns , 2010, Concurr. Comput. Pract. Exp..

[32]  Alexander Shpiner,et al.  Dragonfly+: Low Cost Topology for Scaling Datacenters , 2017, 2017 IEEE 3rd International Workshop on High-Performance Interconnection Networks in the Exascale and Big-Data Era (HiPINEB).

[33]  Leslie G. Valiant,et al.  A Scheme for Fast Parallel Communication , 1982, SIAM J. Comput..

[34]  George F. Riley,et al.  The ns-3 Network Simulator , 2010, Modeling and Tools for Network Simulation.

[35]  Michael Dinitz,et al.  Xpander: Towards Optimal-Performance Datacenters , 2016, CoNEXT.

[36]  Pedro López,et al.  Deterministic versus Adaptive Routing in Fat-Trees , 2007, 2007 IEEE International Parallel and Distributed Processing Symposium.