Clone DAG Synchronization for Distributed Neural Network Training
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Solution Overview
Problem
Current distributed computing methods for training neural networks are limited by the need for heavy user involvement in programming and lack flexibility, especially when changes occur in the configuration or operation of nodes, and they struggle with efficient communication and synchronization across multiple computing devices.
Innovation Solution
A distributed computing system that divides a directed acyclic graph (DAG) among nodes, creates clone DAGs, and uses data exchange vertices to facilitate communication and gradient calculation, enabling efficient traversal and weight updates across multiple computing devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a master-slave model is used for distributed computing, then control and overview are centralized, but flexibility and adaptability deteriorate when configuration changes occur
Solution Approach 1:
The system segments the centralized master-slave control into distributed peer nodes, where each node maintains local oversight of its assigned tasks while participating in collective system-wide coordination through shared state mechanisms
Solution Approach 2:
Each peer node is designed to perform multiple functions simultaneously: executing computational tasks, managing local task queues, participating in gradient synchronization, and maintaining system state, eliminating the need for specialized master/slave roles
2Ease of operation
If heavy user involvement in programming is required for distributed computing, then custom control can be achieved, but ease of operation and reprogramming complexity worsen
Solution Approach 1:
The system implements self-service through automated task allocation algorithms that dynamically assign computational tasks to available nodes based on current system state, eliminating the need for manual programming of task distribution logic
Solution Approach 2:
The system employs dynamic configuration where nodes can be added, removed, or modified at runtime without requiring system reprogramming, as the task allocation and coordination mechanisms automatically adapt to changing system topology
3Productivity
If clone DAGs are created for parallel training, then training efficiency improves, but synchronization complexity and communication overhead increase
Solution Approach 1:
The system merges the synchronization operations of multiple clone DAGs into a single coordinated gradient aggregation step, where gradients from all parallel training instances are combined through centralized parameter servers or distributed gradient accumulation mechanisms
Solution Approach 2:
Parameter servers act as intermediaries between clone DAGs and the central training coordination, handling gradient collection, aggregation, and parameter updates to simplify the synchronization complexity across parallel training instances
4Speed
If data exchange vertices are inserted for node communication, then communication efficiency improves, but device complexity increases
Solution Approach 1:
The system extracts communication operations from the computational DAG and places them in separate data exchange vertices that specialize in inter-node data transfer, allowing communication optimization without complicating the computational logic
Data Source
AI summary
A method is disclosed for synchronization of clone directed acyclic graphs. The method can include identifying a directed acyclic graph (“DAG”) including a plurality of vertices linked in pairwise relationships via a plurality of edges. At least one clone DAG can be created, which at least one clone DAG can be identical to at least a portion of the DAG. For each of the vertices of the DAG, a corresponding clone vertex from the clone vertices of the at least one clone DAG can be identified. Aggregate gradient data can be calculated based on gradient data from each of the clone vertices and its corresponding vertex in the DAG, and at least one weight of the DAG and of the at least one clone DAG can be updated based on the aggregate gradient data.