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Advanced•MLOps
Distributed Training
Parallelism paradigms (DDP, TP, PP, FSDP) and scaling laws.
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Research-Level Deep Dive & Equations
In distributed training, **Distributed Data Parallelism (DDP)** replicates the model across GPU nodes. Each GPU receives a distinct slice of the global batch (a micro-batch) and performs an independent forward and backward pass. The primary engineering challenge is the rapid aggregation of local gradients:
Rather than relying on a centralized Parameter Server which suffers from bandwidth saturation ( communication overhead), modern clusters utilize **Ring-AllReduce**.
In a Ring-AllReduce topology, the nodes are arranged in a logical ring. Let represent the total number of parameter gradients (in bytes). The gradient array is partitioned into equal chunks. The algorithm executes in two successive phases:
1. **Scatter-Reduce**: Over steps, each node sends a chunk to its right neighbor while receiving a chunk from its left neighbor, aggregating the incoming gradients. At the end of this phase, each node holds the fully aggregated gradient sum for exactly one chunk of size .
2. **All-Gather**: Over another steps, the fully aggregated chunks are circulated around the ring so that every node obtains the complete set of aggregated gradients.
**Communication Complexity Analysis**:
The total volume of data transmitted by each GPU throughout the entire Ring-AllReduce process is:
Remarkably, the total data sent per GPU is independent of the number of nodes in the limit as , approaching a bound of . This ensures high linear scaling efficiency.
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