# Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM

**Authors:** Deepak Narayanan, Mohammad Shoeybi, Jared Casper, Patrick LeGresley, Mostofa Patwary, Vijay Anand Korthikanti, Dmitri Vainbrand, Prethvi Kashinkunti, Julie Bernauer, Bryan Catanzaro, Amar Phanishayee, Matei Zaharia  
**arXiv:** [2104.04473](https://arxiv.org/abs/2104.04473)  
**Date:** 2021-04-09  
**Summary page:** https://www.summarizepaper.com/en/paper/2104.04473v5/

## Key points

- Authors address challenges of efficiently training large language models on GPU clusters
- Limited GPU memory capacity and high number of compute operations are obstacles to optimal performance
- Various methods of model parallelism such as tensor and pipeline parallelism proposed to tackle issues
- Different types of parallelism methods (tensor, pipeline, data) can be combined to scale effectively to thousands of GPUs and models with trillions of parameters
- Introduction of novel interleaved pipeline parallelism schedule that enhances throughput by over 10% while maintaining comparable memory footprint
- Quantitative analysis evaluates trade-offs between tensor, pipeline, and data parallelism for insights on configuring distributed training for large models
- Training iterations on a model with 1 trillion parameters achieved at a rate of 502 petaFLOP/s using 3072 GPUs, with per-GPU throughput reaching 52% of theoretical peak performance
- Code openly available at https://github.com/nvidia/megatron-lm
- Research accepted for presentation at SC 2021 contributes valuable insights into optimizing large-scale language model training on GPU clusters

## Summary

In their paper "Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM," authors Deepak Narayanan, Mohammad Shoeybi, Jared Casper, Patrick LeGresley, Mostofa Patwary, Vijay Anand Korthikanti, Dmitri Vainbrand, Prethvi Kashinkunti, Julie Bernauer,
Bryan Catanzaro, Amar Phanishayee and Matei Zaharia address the challenges of efficiently training large language models. They highlight that the limited GPU memory capacity and high number of compute operations required for training pose significant obstacles in achieving optimal performance. Various methods of model parallelism such as tensor and pipeline parallelism have been proposed to tackle these issues. The authors delve into how different types of parallelism methods - tensor, pipeline and data parallelism - can be combined to scale effectively to thousands of GPUs and models with trillions of parameters. They explore techniques for pipeline parallelism and introduce a novel interleaved pipeline parallelism schedule that enhances throughput by over 10% while maintaining a memory footprint comparable to existing approaches. Through quantitative analysis they evaluate the trade-offs between tensor,pipeline,and data parallelism to provide insights on configuring distributed training for large models. Their approach enables them to conduct training iterations on a model with 1 trillion parameters at an impressive rate of 502 petaFLOP/s using 3072 GPUs.The achieved per-GPU throughput reaches 52% of the theoretical peak performance.The authors have made their code openly available at https://github.com/nvidia/megatron-lm.This research has been accepted for presentation at SC 2021 and contributes valuable insights into optimizing large-scale language model training on GPU clusters.