Neural Network Weight Updates Using Parallel Reduce-Scatter Sync
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Solution Overview
Problem
Training neural networks is an intensive process that consumes significant memory, time, and computing resources, necessitating a reduction in these resources to enhance efficiency.
Innovation Solution
Distribute weight updates across multiple workers in parallel, allowing each worker to apply gradients to a subset of weights independently and redundantly, followed by a reduce-scatter operation to sum gradients and an all-gather operation to synchronize updated weights, reducing the time required for weight updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If weight updates are performed sequentially in traditional neural network training, then memory usage is reduced, but training time increases significantly
Solution Approach 1:
The patent divides the weight update operation into multiple independent segments that can be processed in parallel. Each worker process handles a specific portion of the weight updates, allowing simultaneous execution of multiple update operations without requiring all weights to reside in memory at once. This segmentation enables parallel processing while reducing peak memory requirements.
Solution Approach 2:
The patent implements a hierarchical structure where worker processes are nested within a master process coordination framework. The master process manages the overall training loop and coordinates multiple worker processes, each handling subsets of weight updates. This nested architecture allows efficient resource management and parallel execution while maintaining system-wide coherence.
2Productivity
If parallel processing is used to speed up weight updates, then training speed improves, but memory footprint increases
Solution Approach 1:
Each worker process maintains only the local portion of weights and gradients needed for its specific update tasks, rather than duplicating the entire weight matrix. This local quality approach allows parallel processing while keeping individual worker memory footprints small. The master process coordinates these local operations to achieve global weight updates.
Solution Approach 2:
The patent extracts the weight update computation from the main training loop and distributes it to separate worker processes. By taking out the computationally intensive weight update operations and performing them in parallel external to the main sequence, the system achieves speedup without requiring the main process to hold all weights in memory simultaneously.
3Ease of manufacture
If all workers update all weights redundantly, then simplicity of implementation is maintained, but computational efficiency decreases
Solution Approach 1:
The patent segments both the weight matrix and gradient computations across multiple workers. Each worker is assigned specific weight parameters and corresponding gradients to update, eliminating redundant computations. This segmentation maintains implementation simplicity through clear division of labor while dramatically improving computational efficiency by ensuring each computation performs unique useful work.
Data Source
AI summary
Speed of training a neural network is improved by updating the weights of the neural network in parallel. In at least one embodiment, after back propagation, gradients are distributed to a plurality of processors, each of which calculate a portion of the updated weights of the neural network.