Parallel SGD Symbolic Representation for Distributed Model Training
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Solution Overview
Problem
The sequential nature of stochastic gradient descent (SGD) algorithms leads to prolonged computation times and resource-intensive processes when updating model parameters, especially when dealing with large training datasets distributed across multiple geographic locations, resulting in latency and inefficiency in machine learning applications.
Innovation Solution
Implementing a parallel SGD system where processing nodes compute local models in parallel and transmit symbolic representations instead of large training data subsets, allowing for the combination of local models into a global model that reflects the parameters computed sequentially, thus reducing resource usage and latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sequential SGD is used to process training data, then model parameter accuracy is improved, but computation time increases
Solution Approach 1:
The training dataset is divided into multiple partitions distributed across different geographic locations, with each location processing its local partition in parallel to compute local model parameters. This segmentation enables parallel processing while maintaining the sequential SGD update mechanism within each partition, thereby reducing overall computation time without sacrificing model accuracy.
Solution Approach 2:
Symbolic representations serve as intermediaries between distributed processing nodes and the central coordination system. Instead of transmitting large volumes of raw training data or intermediate model states, each node computes and transmits compact symbolic representations that capture essential gradient information, enabling efficient parallel aggregation while preserving sequential SGD semantics.
2Productivity
If training data is distributed across multiple geographic locations, then resource utilization is improved, but network transmission overhead increases
Solution Approach 1:
The invention extracts only the essential symbolic representations from each distributed training node, separating the critical model update information from the bulk training data. This extraction approach allows each geographic location to utilize its local resources for processing while transmitting only minimal necessary information over the network, significantly reducing transmission overhead while maintaining effective distributed training.
Solution Approach 2:
The system transforms the model parameters into symbolic representations that have different computational and transmission characteristics. This parameter transformation reduces the dimensionality and complexity of transmitted data, enabling efficient communication across distributed locations while preserving the mathematical relationships necessary for accurate model training.
3Speed
If parallel processing is implemented, then computation speed is improved, but maintaining sequential dependencies becomes difficult
Solution Approach 1:
The system implements periodic synchronization where distributed processing nodes alternately perform local parallel computation and global aggregation phases. During local phases, nodes independently process their partitions maintaining sequential SGD updates; during aggregation phases, symbolic representations are combined to update global model parameters. This periodic alternation preserves sequential dependencies while enabling parallel processing efficiency.
Solution Approach 2:
The symbolic representation framework enables continuous model improvement by maintaining an ongoing sequence of update operations across distributed nodes. Rather than breaking the sequential dependency chain, the system continues the useful action of parameter refinement through coordinated symbolic computations, ensuring that parallel processing does not interrupt the convergence process.
Data Source
AI summary
Described herein is a system that transmits and combines local models, that individually comprise a set of local parameters computed via stochastic gradient descent (SGD), into a global model that comprises a set of global model parameters. The local models are computed in parallel at different geographic locations along with symbolic representations. The symbolic representations can be used to combine the local models. The global model can determine a likelihood, given a new data instance of a feature set, that a user performs a computer interaction with the content element. For instance, the system can use the model to provide search results in response to a search query submitted by a user. Or, the system can use the model to make a recommendation or suggestion to a user in response to a request for content (e.g., display a targeted advertisement, suggest a news story, etc.).