Hierarchical Distributed Learning Node Coordination
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Solution Overview
Problem
Existing parallel distributed learning methods face challenges in achieving high scalability and efficient learning speed, especially as the number of nodes increases, due to synchronization costs and varying process speeds among nodes.
Innovation Solution
The system employs a hierarchical approach, combining Synchronous-SGD in the first level for each group of nodes and batch size independent parallel processing in the second level between representative nodes of different groups, allowing for non-synchronized communication and adaptive weight updates based on gradient calculation speeds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If the number of nodes is increased to achieve faster learning results, then the learning speed should improve, but the synchronization costs and communication overhead increase causing scalability to deteriorate
Solution Approach 1:
The system segments nodes into multiple groups (first group and second group) with representative nodes that coordinate between groups. This segmentation reduces the synchronization scope from all nodes to only representative nodes, thereby reducing synchronization costs while maintaining parallel processing speed through intra-group synchronization.
Solution Approach 2:
Representative nodes act as intermediaries between groups of nodes. Instead of all nodes synchronizing directly, representative nodes communicate with each other to coordinate learning progress across groups, reducing communication overhead and synchronization complexity while enabling scalable parallel processing.
2Speed
If the number of nodes is increased to achieve faster learning results, then the learning speed should improve, but varying process speeds among nodes cause effectiveness to deteriorate
Solution Approach 1:
The system allows different groups of nodes to operate with different local characteristics and speeds. Representative nodes of different groups can coordinate their learning progress independently, enabling each group to optimize its local processing speed without compromising overall learning effectiveness, thus accommodating varying node speeds.
Solution Approach 2:
The system dynamically adapts the learning process by allowing representative nodes to coordinate progress based on their respective group performance. This dynamic coordination enables the system to handle varying process speeds among nodes effectively, maintaining learning effectiveness while scaling up the number of nodes.
3Reliability
If synchronous processing is used to ensure consistent learning results, then learning effectiveness is maintained, but synchronization costs and batch size requirements increase
Solution Approach 1:
The system segments the synchronous processing requirement by applying synchronization only at the representative node level between groups, rather than requiring all nodes to synchronize simultaneously. This maintains learning effectiveness through coordinated progress while significantly reducing synchronization costs and batch size requirements.
Solution Approach 2:
Representative nodes serve as intermediaries that coordinate learning progress between groups. This intermediary approach maintains learning effectiveness by ensuring consistent progress across groups while reducing the synchronization overhead compared to full node synchronization.
Data Source
AI summary
According to one embodiment, in nth (n is a natural number) processing, a first node calculates a first gradient to update a first weight and a second node calculates a second gradient to update the first weight. In mth (m is a natural number) processing, a third node calculates a third gradient to update a third weight and a fourth node calculates a fourth gradient to update the third weight. If the calculation by the first and second nodes is faster than the calculation by the third and fourth nodes, in n+1th processing, a second weight updated from the first weight is further updated using the first and second gradients, and, in m+1th processing, a fourth weight updated from the third weight is further updated using the first to fourth gradients.