Segment Switching: A New Switching Strategy for Optical HPC Networks
[EN] Photonics are becoming realistic technologies for implementing interconnection networks in near future Exascale supercomputer systems. Photonics present key features to design high-performance and scalable supercomputer networks, such as higher bandwidth and lower latencies than their electroni...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/183291 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/183291 |
| Access Level: | acceso abierto |
| Palabra clave: | Interconnection networks Simulation Photonic technology Exascale supercomputers ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES |
| Sumario: | [EN] Photonics are becoming realistic technologies for implementing interconnection networks in near future Exascale supercomputer systems. Photonics present key features to design high-performance and scalable supercomputer networks, such as higher bandwidth and lower latencies than their electronic supercomputer networks counterparts. Some research work is focused on conventional network topologies built with photonic technologies, with the aim of taking advantage of photonic characteristics. Nevertheless, these approaches fail in that they keep low the network utilization. We looked into this downside and we found that circuit switching was the main performance limitation. In this article we propose a new switching mechanism, called Segment Switching, to address this constraint and improve the network utilization. Segment Switching splits the circuit in segments of the whole path, and uses buffering on selected nodes on the network. Experimental results show that the devised approach signicantly outperforms photonic circuit switching in conventional torus and fat tree networks by 70% and 90%, respectively. |
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