Multi-Step Optimization for High Performance Clusters
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Solution Overview
Problem
Systems with multiple parameters face the challenge of combinatorial explosion, where the rapid growth of possible combinations exceeds available resources, making it impractical to test every unique combination to find optimal parameter values for achieving a key performance indicator (KPI).
Innovation Solution
A method using machine learning to perform dimensionality reduction and cluster analysis, decomposing the initial probability function into single dimension functions, allowing for the identification of optimal parameter value combinations without testing every possible combination, thereby minimizing resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If every possible unique combination of parameter values is tested to determine optimal combination, then the accuracy of finding optimal parameter values is improved, but the resource consumption (memory, processing power, time) increases exponentially due to combinatorial explosion
Solution Approach 1:
The patent segments the parameter space into multiple dimensions, where each dimension corresponds to a specific parameter. Instead of testing all combinations across all parameters simultaneously, the system performs dimensionality reduction by analyzing each parameter dimension separately to identify high-performing segments, then combines these segments to form optimal parameter combinations. This segmentation approach transforms an exponentially complex problem into a series of manageable linear analyses.
Solution Approach 2:
The patent applies dimensionality reduction techniques to transform the high-dimensional parameter space into a lower-dimensional representation. By projecting the multi-parameter combinations onto reduced dimensions and performing cluster analysis, the system identifies patterns and optimal combinations without exhaustively testing every point in the original high-dimensional space. This dimensional transformation maintains the essential relationships while dramatically reducing computational requirements.
2Manufacturing precision
If the number of parameter values for each parameter is increased to improve optimization precision, then the accuracy of determining optimal parameter combinations is improved, but the problem space size grows rapidly making exhaustive testing impractical
Solution Approach 1:
The patent performs preliminary dimensionality reduction and cluster analysis on each parameter dimension before combining them into full parameter combinations. By pre-processing each dimension to identify and retain only the most significant parameter values and relationships, the system reduces the effective problem space size. This preliminary action filters out redundant combinations early in the process, allowing high optimization precision to be achieved with a manageable number of actual tests.
Solution Approach 2:
The patent dynamically adjusts the effective number of parameter values considered in each dimension based on performance data and cluster analysis results. Rather than uniformly testing all possible values across all parameters, the system identifies and focuses on the subset of parameter values that contribute most to optimal performance in each dimension. This selective parameter approach maintains optimization precision while dramatically reducing the total problem space that must be explored.
Data Source
AI summary
A method of machine learning includes performing dimensionality reduction on a parameter space by performing initial tests to determine scores for a plurality of parameter values in the parameter space, determining aggregate scores for a plurality of parameter value combinations, determining a ranking of the plurality of parameter value combinations based on the aggregate scores, and performing cluster analysis on the plurality of parameter value combinations to determine a set having highest aggregate scores. The method further includes performing additional tests, wherein each additional test is for a parameter value combination in the set. For each such parameter value combination, a probability of achieving a key performance indicator (KPI) is computed. Cluster analysis is then performed to determine a first subset of the set having highest probabilities of achieving the KPI. An operation is then performed on the first subset.