RAMP: A Flat Nanosecond Optical Network and MPI Operations for Distributed Deep Learning Systems

• URL: http://arxiv.org/abs/2211.15226v1
• Date: Mon, 28 Nov 2022 11:24:51 GMT
• Title: RAMP: A Flat Nanosecond Optical Network and MPI Operations for Distributed Deep Learning Systems
• Authors: Alessandro Ottino, Joshua Benjamin, Georgios Zervas
• Abstract summary: We introduce a near-exascale, full-bisection bandwidth, all-to-all, single-hop, all-optical network architecture with nanosecond reconfiguration called RAMP. RAMP supports large-scale distributed and parallel computing systems (12.8Tbps per node for up to 65,536 nodes.
• Score: 68.8204255655161
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Distributed deep learning (DDL) systems strongly depend on network performance. Current electronic packet switched (EPS) network architectures and technologies suffer from variable diameter topologies, low-bisection bandwidth and over-subscription affecting completion time of communication and collective operations. We introduce a near-exascale, full-bisection bandwidth, all-to-all, single-hop, all-optical network architecture with nanosecond reconfiguration called RAMP, which supports large-scale distributed and parallel computing systems (12.8~Tbps per node for up to 65,536 nodes). For the first time, a custom RAMP-x MPI strategy and a network transcoder is proposed to run MPI collective operations across the optical circuit switched (OCS) network in a schedule-less and contention-less manner. RAMP achieves 7.6-171$\times$ speed-up in completion time across all MPI operations compared to realistic EPS and OCS counterparts. It can also deliver a 1.3-16$\times$ and 7.8-58$\times$ reduction in Megatron and DLRM training time respectively} while offering 42-53$\times$ and 3.3-12.4$\times$ improvement in energy consumption and cost respectively.

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