Intelligent Data Partitioning for Distributed ML Systems
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Solution Overview
Problem
Distributed machine learning systems face inefficiencies due to high network bandwidth requirements and performance degradation from synchronization barriers when handling large-scale tasks with complex models, as existing systems rely on a shared global model paradigm.
Innovation Solution
Implementing a cluster-based or quorum-based data partition module that assigns training and query data to specific ML workers based on clustering algorithms, allowing each worker to build independent models and reducing synchronization overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If a shared global ML model paradigm is used in distributed systems, then model consistency is maintained, but network bandwidth is saturated and synchronization overhead increases
Solution Approach 1:
The patent segments the global model into multiple local models, each maintained by different ML workers. Instead of synchronizing a single global model across all workers, each worker maintains its own local model that is trained on locally processed data, eliminating the need for continuous global model synchronization and reducing network bandwidth consumption.
Solution Approach 2:
The patent implements local quality by allowing different ML workers to have different local models optimized for their specific data distributions and query patterns. Each worker's model is tailored to local characteristics rather than forcing a single global model on all workers, improving local performance while reducing synchronization requirements.
2Stability of the object's composition
If synchronization barriers are implemented for sequential ML algorithms in distributed systems, then model consistency is maintained, but performance degrades due to communication latency
Solution Approach 1:
The patent divides the sequential ML processing into independent segments that can be executed in parallel across different workers. By segmenting the workload and eliminating synchronization barriers between segments, the system achieves parallel execution without compromising model consistency within each segment.
Solution Approach 2:
The patent performs preliminary actions by pre-processing and partitioning data before distribution to workers, and pre-configuring local models with necessary initial parameters. This allows workers to independently process data without needing synchronous coordination during execution, eliminating performance-degrading barriers.
3Measurement precision
If a shared global ML model is synchronized across all ML workers, then uniform classification results are achieved, but communication latency increases
Solution Approach 1:
The patent segments the classification task by assigning different data partitions to different workers, each with its own local model. This segmentation allows parallel processing of different data segments without requiring synchronization for uniform results, as each segment is processed independently with locally optimized models.
Solution Approach 2:
The patent creates local copies of model parameters and architecture from the global model, but allows these copies to diverge and be optimized locally. Each worker maintains a copy of the model that is independently trained on local data, eliminating the need for continuous synchronization while maintaining classification quality through local adaptation.
4Productivity
If distributed systems process large-scale ML tasks with complex models, then processing capacity is increased, but coordination overhead among workers increases
Solution Approach 1:
The patent segments both the data and the model processing across multiple workers, with each worker handling a specific partition independently. This segmentation of responsibilities eliminates the need for complex coordination mechanisms, as each worker operates autonomously on its assigned data segment with its own local model.
Solution Approach 2:
The patent implements self-service by enabling each ML worker to independently process its assigned data partition using its local model without requiring coordination with other workers. Each worker autonomously performs data preprocessing, model inference, and local optimization, dramatically reducing system coordination complexity while maintaining high processing capacity.
Data Source
AI summary
Techniques for implementing intelligent data partitioning for a distributed machine learning (ML) system are provided. In one set of embodiments, a computer system implementing a data partition module can receive a training data instance for a ML task and identify, using a clustering algorithm, a cluster to which the training data instance belongs, the cluster being one of a plurality of clusters determined via the clustering algorithm that partition a data space of the ML task. The computer system can then transmit the training data instance to a ML worker of the distributed ML system that is assigned to the cluster, where the ML worker is configured to build or update a ML model using the training data instance.