Computational Architecture Analysis Using Runtime Metric Distributions
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Solution Overview
Problem
Existing engineering tools lack the ability to effectively analyze and predict the performance metrics of complex computing applications, such as AI applications, before implementation, leading to inefficiencies and resource wastage.
Innovation Solution
A computer-implemented method for analyzing computational architectures by instrumenting software code, collecting runtime data, and constructing a model to predict relevant metrics, including simulating random behavior and estimating distribution parameters for hardware and software components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing engineering tools are used to analyze computing applications, then the analysis process is simple, but the ability to predict performance metrics accurately is insufficient
Solution Approach 1:
The analysis tool segments the computational architecture into discrete components (nodes, edges, operations) and analyzes each separately before aggregating results. This allows complex performance prediction to be broken down into manageable analysis units, improving accuracy without overwhelming complexity.
Solution Approach 2:
The system performs preliminary analysis by extracting architectural information before actual execution. By analyzing the computational graph, data types, and operation characteristics in advance, the tool can predict performance metrics without requiring complex runtime instrumentation, thus improving prediction accuracy while maintaining relative simplicity.
2Loss of energy
If computational architectures are analyzed before implementation, then resource wastage is reduced, but existing tools lack the necessary analysis capabilities
Solution Approach 1:
The system creates a virtual representation (copy) of the computational architecture as a computational graph. This virtual model can be analyzed extensively before implementation, allowing prediction of performance metrics and identification of resource requirements without wasting physical resources on unoptimized implementations.
Solution Approach 2:
The patent replaces traditional runtime performance measurement (mechanical approach) with static architectural analysis (information processing approach). By extracting and analyzing architectural information from the computational graph, the system can predict performance characteristics without needing to actually execute the computation, thus avoiding resource wastage while providing measurement capabilities.
3Measurement precision
If detailed performance analysis is performed, then prediction accuracy improves, but the analysis time and computational overhead increase
Solution Approach 1:
The system performs partial analysis focused on the most critical aspects of the computational architecture - identifying key operations, data types, and computational patterns. This selective analysis approach provides sufficient prediction accuracy for performance optimization without requiring exhaustive analysis of every detail, thus reducing analysis time while maintaining useful precision.
Data Source
AI summary
Systems and methods for estimating a random distribution for an overall metric for a composite node, the composite node comprising a plurality of nodes. For each data atom of a plurality of data atoms being input to the composite node, and for each node of the plurality of nodes, at least one value may be generated for a per-node metric with respect to the data atom. A value for the overall metric with respect to the data atom may be generated based on the per-node metric values of the plurality of nodes. At least one parameter of the random distribution for the overall metric for the composite node may be estimated based on the overall metric values with respect to the plurality of data atoms.