Parallel Neural Network DAG Coordination for Flexible Distributed Training
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Solution Overview
Problem
Current distributed computing methods for training neural networks require significant user involvement and are inflexible, necessitating re-programming for configuration changes, and lack efficient mechanisms for data communication between nodes.
Innovation Solution
A distributed computing system that divides and traverses directed acyclic graphs (DAGs) across multiple nodes, using data exchange vertices to facilitate communication and gradient calculation, enabling synchronous or asynchronous updates of weights in cloned DAGs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a master-slave distributed computing model is used, then control and coordination of computing operations is achieved, but flexibility is reduced and re-programming is required for configuration changes
Solution Approach 1:
The system creates clone DAGs that are identical copies of the original DAG structure. These clones allow parallel execution without requiring re-programming, as the cloning process automatically replicates the computational graph. This resolves the contradiction by enabling flexibility through copying rather than re-programming.
Solution Approach 2:
The system dynamically creates and manages clone DAGs based on training requirements. The ability to dynamically instantiate clones without re-programming provides flexibility while maintaining the structured control of the original DAG. This dynamic approach eliminates the need for static, pre-programmed configurations.
2Ease of operation
If significant user involvement is required for programming slave operations and master control, then customized computing operations can be achieved, but ease of operation is reduced
Solution Approach 1:
The system performs self-service through automatic clone creation and management. The master node automatically creates and coordinates clone DAGs without requiring user intervention in programming slave operations. This self-service mechanism improves ease of operation while maintaining high automation levels.
Solution Approach 2:
The system segments the computing workload into independent clone DAGs that can be automatically managed. Each clone is a self-contained unit that can be created and executed without manual programming, reducing the burden on users while maintaining automation.
3Productivity
If data communication mechanisms between nodes are not optimized, then system simplicity is maintained, but training efficiency is reduced
Solution Approach 1:
The system introduces data exchange vertices as intermediaries between nodes in the DAG. These vertices facilitate efficient data communication without requiring complex custom communication mechanisms. The intermediary approach improves training efficiency by providing structured data flow paths while keeping the communication mechanism relatively simple.
4Productivity
If clone DAGs are used for parallel training, then training speed is improved, but gradient aggregation complexity increases
Solution Approach 1:
The system merges gradients from multiple clone DAGs through aggregation at the master node. This merging process combines the results from parallel clones into a unified weight update, improving training speed through parallelism while managing aggregation complexity through systematic combination of gradient data.
Data Source
AI summary
A method is disclosed for training and utilizing massively parallel neural networks. A distributed computing system may be configured to perform various operations. The distributed computing system may divide a directed acyclic graph (“DAG”) that comprises a plurality of vertices linked in pairwise relationships via a plurality of edges among a plurality of nodes. Each node may comprise a computing device. The distributed computing system may provide a map of the DAG that described a flow of data through the vertices to each of the vertices of the DAG. The distributed computing system may perform a topological sort of the vertices of the DAG and may traverse the DAG.