Matrix Multiplication API Using Preclassification for Algorithm Choice
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Solution Overview
Problem
Determining appropriate algorithms for matrix operations consumes significant memory, time, and computing resources.
Innovation Solution
A matrix multiplication algorithm library accessible through an API that selects optimal algorithms based on input parameters, allowing entities to specify attributes and preferences, and provides a list of high-performance algorithms for matrix operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional methods are used to determine appropriate algorithms for matrix operations, then comprehensive algorithm selection is possible, but significant memory, time, and computing resources are consumed
Solution Approach 1:
The system performs preliminary classification of matrix operations into categories (e.g., dense, sparse, structured) and pre-associates each category with suitable algorithm families. This preliminary action avoids the need to evaluate all possible algorithms from scratch for each operation, significantly reducing the time and resources required for algorithm selection while maintaining adaptability.
Solution Approach 2:
The algorithm selection process is segmented into multiple stages: first categorizing the matrix operation type, then selecting candidate algorithms based on the category, and finally choosing the optimal algorithm based on specific parameters. This segmentation divides the complex selection task into manageable steps, reducing overall computational overhead.
2Adaptability or versatility
If traditional methods are used to determine appropriate algorithms for matrix operations, then comprehensive algorithm selection is possible, but significant memory and computing resources are consumed
Solution Approach 1:
The system pre-computes and stores classification rules and algorithm mappings in compact data structures that require minimal memory. By performing classification and candidate selection in advance based on operation categories, the system avoids storing and processing large amounts of algorithm specification data during actual matrix operations.
Solution Approach 2:
The invention extracts only the essential characteristics needed for algorithm selection (matrix type, operation type, size parameters) and uses these extracted features to determine the appropriate algorithm. This extraction approach avoids processing and storing complete algorithm specifications for all possible matrix operations, significantly reducing memory requirements.
3Productivity
If optimal algorithms are selected for matrix operations, then execution efficiency is improved, but additional computational overhead is required for algorithm determination
Solution Approach 1:
The system performs algorithm classification and selection in advance, storing the results in accessible data structures. This preliminary action ensures that during actual matrix operations, the system only needs to retrieve pre-determined algorithm specifications rather than performing complex selection logic, minimizing the computational overhead at execution time while maintaining high productivity.
Data Source
AI summary
Apparatuses, systems, and techniques to determine a matrix multiplication algorithm for a matrix multiplication operation. In at least one embodiment, a matrix multiplication operation is analyzed to determine an appropriate matrix multiplication algorithm to perform the matrix multiplication algorithm.