Neural Network Checkpoint Splitting Across Nodes for Lower I/O Overhead
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Solution Overview
Problem
In large-scale distributed processing systems, checkpointing and restarting neural networks incur significant input/output and network overhead due to frequent storage and retrieval of checkpoint files.
Innovation Solution
A method and apparatus that determine the number of splits for checkpoint files based on available resource quantity of nodes, storing these splits in local storage devices, and managing meta and parity information in remote storage to minimize overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If checkpoint files are stored in a single centralized location, then retrieval is simple, but I/O and network overhead increase significantly in large-scale distributed systems
Solution Approach 1:
The patent divides the centralized checkpoint storage into multiple distributed segments across different nodes in the cluster. Each node stores a portion of checkpoint data locally, eliminating the single-point bottleneck and reducing network I/O overhead during checkpoint operations.
Solution Approach 2:
The patent transitions from a single-dimension centralized storage model to a multi-dimensional distributed storage architecture across the cluster network. Checkpoint data is replicated and stored across multiple spatial dimensions (nodes), enabling parallel access and reducing network traffic concentration.
2Loss of energy
If checkpoint files are split and stored across multiple nodes, then network overhead is reduced, but determining the number of splits and managing distribution becomes complex
Solution Approach 1:
The patent implements self-service mechanisms where nodes automatically determine their own checkpoint storage responsibilities based on available resources. The system autonomously calculates the number of splits and distributes checkpoints without requiring complex external coordination, reducing management overhead.
Solution Approach 2:
The patent employs feedback mechanisms where nodes report their resource availability (storage capacity, computational power) to the system, which then dynamically adjusts the number of checkpoint splits and distribution strategy. This closed-loop control simplifies management by adapting to current system state.
3Reliability
If more nodes are used for checkpoint storage, then reliability improves, but resource utilization and storage efficiency decrease
Solution Approach 1:
The patent dynamically changes the replication factor and storage parameters based on system conditions. The number of checkpoint copies stored across nodes is adjusted as a variable parameter rather than a fixed value, optimizing the balance between reliability and storage efficiency according to available resources and failure risk.
Solution Approach 2:
The patent applies partial replication strategies where not all checkpoint data is replicated to all nodes. Instead, critical portions are replicated while less critical data uses more efficient storage, achieving sufficient reliability without excessive resource consumption.
Data Source
AI summary
A processor-implemented method includes generating a checkpoint file of a neural network, determining, based on an available resource quantity of a group comprising nodes performing an operation corresponding to the checkpoint file, the number of splits of the checkpoint file, and storing the determined number of splits of the checkpoint file in the nodes in the group, respectively.