AI Model Partitioning for Heterogeneous Distributed Training
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Solution Overview
Problem
Distributed training of deep learning models in heterogeneous computing environments faces challenges such as synchronization overhead, resource imbalance, and delays due to differences in computing performance and network speed among nodes in a computer cluster.
Innovation Solution
An electronic device with a communication interface, memory, and processor that segments AI models and training data based on node profiles, distributes them optimally, converts data formats, and adjusts data sizes to ensure efficient training across multiple computation nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If distributed training is performed by using a computer cluster with heterogeneous nodes, then training capability is improved, but synchronization overhead and resource imbalance occur
Solution Approach 1:
The patent segments the AI model into multiple parts distributed across different computation nodes, with each node processing specific model portions independently. This segmentation allows heterogeneous nodes to contribute differently without requiring synchronization of all operations, reducing the synchronization overhead while maintaining overall training capability.
Solution Approach 2:
The patent assigns different computational tasks to different computation nodes based on their specific capabilities and characteristics. Each node processes data and model parameters according to its local quality and performance level, allowing heterogeneous nodes to operate optimally without forcing uniform processing that causes synchronization delays.
2Productivity
If distributed training is performed by using a computer cluster with heterogeneous nodes, then training capability is improved, but resource imbalance occurs
Solution Approach 1:
The patent dynamically adjusts the distribution of training data and model parameters across computation nodes based on real-time performance monitoring. The system adapts resource allocation to match the actual computational capacity of each node, preventing resource imbalance while maximizing training capability utilization.
Solution Approach 2:
The patent changes the parameters of data partitioning and model distribution based on the performance characteristics of individual computation nodes. By adjusting these parameters dynamically, the system optimizes resource distribution across heterogeneous nodes, ensuring each node receives appropriate workload matching its capabilities.
3Productivity
If distributed training is performed by using a computer cluster, then training capability is improved, but delay in acquisition of computation results occurs
Solution Approach 1:
The patent performs preliminary actions by pre-partitioning the training data and model parameters before distribution to computation nodes. This preliminary segmentation allows nodes to process and return results independently without waiting for all nodes to complete, accelerating result acquisition while maintaining overall training capability.
Solution Approach 2:
The patent implements feedback mechanisms where computation nodes report their processing status and results to the coordinating node. This feedback allows the system to track progress and optimize data distribution to minimize delays in result acquisition, balancing training capability with speed requirements.
4Adaptability or versatility
If data format conversion is performed for heterogeneous computation nodes, then compatibility is improved, but processing time increases
Solution Approach 1:
The patent performs data format conversion as a preliminary action before data is distributed to computation nodes. By converting data to the appropriate formats in advance based on node profiles, the system ensures compatibility without adding conversion time during the critical training processing phase.
Solution Approach 2:
The patent applies different data format conversion strategies to different computation nodes based on their specific requirements. Each node receives data in its locally optimized format, minimizing conversion overhead while ensuring compatibility across the heterogeneous cluster.
Data Source
AI summary
An electronic device performing distributed training of an artificial intelligence (AI) model in a heterogeneous computing environment includes a communication interface for communicating with multiple computation nodes; a memory storing profile information on the multiple computation nodes and instructions; and at least one processor configured to assign weights to each of the multiple computation nodes for segmenting the AI model and training data based on the profile information; distribute the segmented AI model and training data to the multiple computation nodes based on the assigned weights; control the multiple computation nodes to train the segmented AI model; convert training result data from a first computation node into a data format processible by other computation nodes; and control a second computation node to train the segmented AI model based on the converted data.