Reference library
Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM
Paper summary
Combines tensor, pipeline, and data parallelism for training very large language models across GPU clusters. An interleaved pipeline schedule reduces idle time, with throughput measurements illustrating scaling constraints.
Relevance to the project
Useful for future profiling of batching, synchronization, and work partitioning. Present needs are a single-engine prototype, so cluster-scale results are a distant engineering reference.
Training throughput is not per-iteration simulation latency.
Source record
- Reference ID
- megatron-scaling
- Archived version
- 2104.04473v5
- Summary provenance
- reference-library/papers/megatron-scaling/metadata.json · summary
- Review status
- initial orientation summary; not a critical full-paper review
- Reading scope
- Selected abstract/opening and available closing discussion/conclusion passages in the preserved text extract; broader project notes provide context. No reproduction of experiments, exhaustive methods review, or equation/figure audit.