Distributed Parameter Calculation for Iterative Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current higher-order methods for objective function determination, such as L-BFGS, face significant computational and storage challenges when dealing with very large data sets, leading to high costs and inefficiencies in parallel processing, especially due to the need to distribute and process the entire parameter vector across multiple worker processors.
Innovation Solution
The system divides the parameter set among multiple worker computers, with each performing sub-processes on a unique subset of the parameter set and training data, using a minimal set of instruction primitives, and communicating results to a master computer for overall optimization, allowing for distributed and efficient calculation of parameter updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the entire parameter vector is distributed to each worker processor for parallel processing, then the optimization can be performed using higher-order methods, but the computational cost and storage requirements increase significantly
Solution Approach 1:
The parameter vector is divided into multiple parameter subsets, with each worker processor receiving only its assigned subset rather than the complete parameter vector. This segmentation reduces the storage burden on each processor while enabling parallel optimization through higher-order methods that require local parameter information.
2Reliability
If the entire parameter vector is distributed to each worker processor, then complete optimization can be performed, but the computational cost increases significantly
Solution Approach 1:
The optimization problem is segmented into sub-problems, each handled by a worker processor operating on its parameter subset. This allows higher-order optimization methods to be applied locally with reduced computational cost, while the master processor coordinates the overall optimization to maintain accuracy.
Solution Approach 2:
The master processor acts as an intermediary that coordinates the distributed optimization process. It manages the parameter subsets, aggregates results from worker processors, and ensures the overall optimization accuracy is maintained despite the distributed approach.
3Adaptability or versatility
If each worker computer has a complete parameter vector, then independent optimization sub-processes can be performed, but scalability is limited due to storage and computation constraints
Solution Approach 1:
The parameter set is divided into multiple subsets that can be distributed across any number of worker processors. This segmentation enables the system to scale by simply adding more workers, each handling a portion of the parameters, without requiring each worker to store the complete parameter vector.
Solution Approach 2:
The master processor provides universal coordination functionality, managing parameter distribution, collecting results, and coordinating optimization across different numbers and configurations of worker processors. This universal coordination mechanism enables flexible system configuration and scaling.
Data Source
AI summary
Systems and methods are disclosed for distributed first- or higher-order model fitting algorithms. Determination of the parameter set for the objective function is divided into a plurality of sub-processes, each performed by one of a plurality of worker computers. A master computer coordinates the operation of the plurality of worker computers, each operating on a portion of the parameter set such that no two worker computers contain exactly the same parameter subset nor the complete parameter set. Each worker computer performs its sub-processes on its parameter subset, together with training data. For maximum efficiency, the sub-processes are performed using a compact set of instruction primitives. The results are evaluated by the master computer, which may coordinate additional sub-process operations to perform higher-order optimization or terminate the optimization method and proceed to formulation of a model function.