Automated Clone DAG Synchronization for Neural Network Training
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Solution Overview
Problem
Current distributed computing systems for neural networks require significant user involvement in programming and are inflexible, necessitating re-programming with even minor changes, and lack efficient mechanisms for loading weights across different machines and input sizes.
Innovation Solution
Implement a distributed computing system that utilizes clone directed acyclic graphs (DAGs) to synchronize and traverse neural networks across multiple nodes, incorporating data exchange vertices for efficient data transfer and gradient calculation, enabling asynchronous updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a master-slave distributed computing model is used, then control and coordination of computing operations are improved, but flexibility and adaptability deteriorate due to heavy re-programming requirements
Solution Approach 1:
The patent creates clone DAGs that are identical copies of the original DAG structure. These clones enable parallel processing while maintaining the same computational logic, allowing the system to scale without re-programming. The clones can be distributed across multiple nodes and executed simultaneously, providing both control (through the master DAG) and flexibility (through independent clone execution).
Solution Approach 2:
The patent segments the computational graph into multiple independent clone DAGs that can be executed in parallel. Each clone represents a segment of the overall computation that can be processed independently on different nodes, improving flexibility while maintaining coordination through the master DAG structure.
2Productivity
If clone DAGs are used for parallel processing, then productivity and processing speed are improved, but synchronization complexity and difficulty of detecting and measuring increase
Solution Approach 1:
The patent implements gradient synchronization mechanisms where gradient data flows back from clone DAGs to the master DAG. This feedback loop enables automatic coordination of weight updates across all clones, reducing synchronization complexity. The system automatically detects when all clones have completed their forward and backward passes and coordinates the aggregation of gradients without requiring complex manual synchronization logic.
Solution Approach 2:
The patent performs preliminary actions by pre-defining the DAG structure and clone relationships before execution. The master DAG establishes the computational template that all clones will follow, allowing the system to prepare synchronization points and data exchange vertices in advance, thereby reducing the complexity of real-time synchronization during parallel execution.
3Manufacturing precision
If extensive re-programming is required for configuration changes, then manufacturing precision and control are improved, but ease of operation and adaptability deteriorate
Solution Approach 1:
The patent creates a universal DAG template that can be cloned and executed multiple times with different parameters. The master DAG structure serves as a reusable blueprint that maintains precise control over computational logic while allowing easy adaptation through parameter changes and clone creation, eliminating the need for extensive re-programming when configurations change.
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.