Echo: Simulating Distributed Training At Scale

• URL: http://arxiv.org/abs/2412.12487v1
• Date: Tue, 17 Dec 2024 02:44:35 GMT
• Title: Echo: Simulating Distributed Training At Scale
• Authors: Yicheng Feng, Yuetao Chen, Kaiwen Chen, Jingzong Li, Tianyuan Wu, Peng Cheng, Chuan Wu, Wei Wang, Tsung-Yi Ho, Hong Xu,
• Abstract summary: We build Echo to tackle three key challenges in large-scale training simulation. Echo delivers on average 8% error in training step -- roughly 3x lower than state-of-the-art simulators.
• Score: 20.589262446733077
• License:
• Abstract: Simulation offers unique values for both enumeration and extrapolation purposes, and is becoming increasingly important for managing the massive machine learning (ML) clusters and large-scale distributed training jobs. In this paper, we build Echo to tackle three key challenges in large-scale training simulation: (1) tracing the runtime training workloads at each device in an ex-situ fashion so we can use a single device to obtain the actual execution graphs of 1K-GPU training, (2) accurately estimating the collective communication without high overheads of discrete-event based network simulation, and (3) accounting for the interference-induced computation slowdown from overlapping communication and computation kernels on the same device. Echo delivers on average 8% error in training step -- roughly 3x lower than state-of-the-art simulators -- for GPT-175B on a 96-GPU H800 cluster with 3D parallelism on Megatron-LM under 2 minutes.

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