Distributed Gaussian Process Classification System
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Gaussian process classification with Laplacian approximation is computationally inefficient for large datasets, making it unworkable with datasets containing more than a million observations due to excessive computing time.
Innovation Solution
A distributed classification model training system that uses a distributed Laplacian approximation inference method, balancing data across multiple computing devices for efficient computation, reducing hardware requirements and improving algorithm speed without compromising accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Gaussian process classification with Laplacian approximation is used, then classification accuracy is improved, but computing time becomes excessive for large datasets
Solution Approach 1:
The training dataset is divided into multiple partitions, each assigned to a separate worker computing device. Each worker independently computes kernel matrix blocks and objective function values for its partition, enabling parallel processing that reduces overall computing time while maintaining classification accuracy through coordinated aggregation of results across all workers.
2Ease of manufacture
If traditional single-device Gaussian process classification is used, then implementation simplicity is maintained, but hardware requirements become prohibitive for large datasets
Solution Approach 1:
The system transitions from a single-device architecture to a distributed multi-device architecture, adding the dimension of spatial distribution across multiple computing devices. This enables the system to handle large datasets by distributing computational and memory loads across multiple machines, thereby reducing the hardware burden on any single device while maintaining implementation feasibility.
3Productivity
If distributed computing is implemented, then processing speed is improved, but system complexity increases
Solution Approach 1:
A controller device serves as an intermediary that coordinates communication between worker computing devices. The controller distributes data partitions to workers, aggregates computed results, and manages the overall training process. This intermediary architecture simplifies the complexity by providing a centralized coordination point, allowing workers to focus on independent computations while the controller handles inter-device communication and result integration.
Data Source
AI summary
A computing system trains a classification model using distributed training data. A first worker index and a second worker index are received from a controller device and together uniquely identify a segment of a lower triangular matrix. The first and second worker indices have values from one to a predefined block size value. In response to receipt of a first computation request from the controller device, a first kernel matrix block is computed at each computing device based on the first worker index and the second worker index. In response to receipt of a second computation request from the controller device, an objective function value is computed for each observation vector included in an accessed training data subset. The computed objective function value is sent to the controller device. Model parameters for a trained classification model are output.