Chapter 2
Parallelism Fundamentals in Megatron-LM
Scaling language models to billions or trillions of parameters is only possible through sophisticated parallelism strategies that push hardware and software to their limits. In this chapter, we dissect the core parallelization principles behind Megatron-LM, revealing how data, model, and hybrid parallel techniques interlock to maximize throughput and enable training at scales that would otherwise be intractable. Each section pulls back the curtain on the intricate balancing act of computation, communication, and memory across distributed systems-transforming clusters of devices into a single, cohesive learning engine.
2.1 Data Parallelism: Concepts and Implementations
Data parallelism is a cornerstone technique in scaling deep learning model training across multiple devices or nodes. Fundamentally, data parallelism distributes portions of the training dataset across a set of processors that maintain replicas of the model parameters. Each processor independently computes gradients on its allocated mini-batch, and these gradients are then synchronized to ensure consistent model updates. This approach leverages the natural independence of data samples during the forward and backward passes while carefully orchestrating the aggregation of computed gradients.
Megatron-LM exemplifies a sophisticated implementation of data parallelism tailored for training extremely large language models. Its architecture partitions training data evenly across multiple GPUs, enabling concurrent computation while addressing the challenges of synchronization, reproducibility, and efficient communication. The primary objective is to maintain model consistency across devices without incurring prohibitive communication overheads or sacrificing numerical determinism.
Partitioning Training Data
In Megatron-LM, the global training dataset is split into distinct shards, each assigned to a separate data-parallel rank (process). Each rank holds a full copy of the model parameters and processes a unique subset of input samples per iteration. This exclusive partitioning eliminates redundant work and ensures that each data point influences the model update exactly once per epoch. The data-loading pipeline preprocesses and distributes batches, often employing deterministic shuffling with consistent random seeds to maintain reproducibility.
A critical design decision involves the batch size per GPU, which directly impacts both convergence properties and hardware utilization. Larger local batch sizes improve compute efficiency but may reduce gradient update frequency; smaller batches increase synchronization overhead. Megatron-LM typically balances these factors, leveraging mixed-precision training and accumulation to optimize throughput.
Synchronization Mechanisms
After each forward and backward pass, the computed gradients must be aggregated across all data-parallel ranks to maintain a consistent parameter state. Megatron-LM employs all-reduce collective communication primitives to sum gradients efficiently across devices. The classic algorithm involves summing and redistributing gradients so that every model replica obtains an identical, averaged gradient tensor before applying the weight updates.
This synchronization is implemented using communication libraries such as NVIDIA's NCCL or MPI, chosen for their hardware-level optimizations and scalability. Collective routines reduce latency by overlapping communication with computation and by exploiting hierarchical network topologies-e.g., NVLink within nodes and InfiniBand across nodes.
Formally, let each rank i compute gradients gi on its batch. The all-reduce operation computes:
where N is the number of data-parallel ranks. Subsequently, each rank updates its local copy of parameters
