CPU Core Scaling Domain Optimization via Quantified Scalability
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Solution Overview
Problem
Modern CPU designers face challenges in improving performance as increased core counts do not automatically translate to better performance unless system code is designed to be scalable, and there is a lack of effective methods to quantify performance enhancement from scaling.
Innovation Solution
A system and method that calculates the optimal number of CPU cores for scaling domains using quantified scalability measurements and linear regression, estimating parallel fractions and speedup characteristics to determine the number of CPU cores for each domain, thereby optimizing system performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If increased core counts are placed onto the same die, then CPU manufacturing capability is improved, but performance improvement is not automatic unless system code is designed to be scalable
Solution Approach 1:
The patent segments the CPU cores into different scaling domains (first scaling domain and second scaling domain) with different numbers of cores (n and N-n respectively). This segmentation allows the system to optimize performance by distributing workloads across domains with different parallelization characteristics, rather than using a single monolithic core configuration. The segmentation principle resolves the contradiction by enabling selective scalability - each domain can be optimized independently for its specific workload requirements.
Solution Approach 2:
The patent changes the parameter of core allocation by calculating optimal values for n (first domain cores) and N-n (second domain cores) based on quantified scalability measurements. By dynamically adjusting the number of cores in each scaling domain based on measured parallel fraction and speedup characteristics, the system achieves optimal performance without requiring all cores to be designed for maximum scalability. This parameter optimization resolves the contradiction between raw core count and actual performance improvement.
2Productivity
If system code is designed to be scalable, then performance improvement from multiple cores is achieved, but there is a lack of effective methods to quantify performance enhancement from scaling
Solution Approach 1:
The patent implements a feedback mechanism by measuring actual performance values XD1(n) and XD2(N-n) from processing iterations and using these measurements to calculate quantified scalability metrics (parallel fraction and speedup characteristic). These measured values are then fed back into the quadratic equation to determine optimal core allocation. This closed-loop feedback system resolves the measurement precision contradiction by providing objective, empirically-based quantification of scalability rather than relying on theoretical models alone.
Solution Approach 2:
The patent replaces subjective or theoretical scalability assessment with an objective mathematical model based on quadratic equations and linear regression. By substituting mechanical/intuitive core allocation approaches with a formal mathematical framework that processes measured performance data, the system achieves precise quantification of scalability. The quadratic equation provides a rigorous method to determine optimal core distribution based on measured parallel fraction and speedup characteristics.
3Ease of operation
If CPU cores are partitioned into scaling domains, then objective cost-performance tradeoffs are enabled, but calculation complexity increases
Solution Approach 1:
The patent simplifies the optimization problem by changing parameters through mathematical transformation. The quadratic equation is derived from the linear relationship between parallel fraction and speedup characteristic, reducing the complex multi-variable optimization problem into a single-variable quadratic equation. This parameter transformation makes the calculation tractable while maintaining the ability to optimize cost-performance tradeoffs. The mathematical model converts complex performance measurement data into a solvable quadratic equation with clear optimal solutions.
Data Source
AI summary
Systems and methods for performance tuning a computer system in scaling domains based on quantified scalability. A system includes a processor configured to: calculate an estimate of parallel fraction and speedup characteristic in a first domain D1 and in a second domain D2 for an application, the estimate being calculated using system performance measurements generated from previous processing iterations of one or more workloads of the application using a number, n, of cores in the first domain and a remaining number, N−n, of cores in the second domain to obtain performance values XD1(n) and XD2(N−n), wherein N represents a total number of cores; calculate the number of cores for the first domain using a quadratic equation generated from the parallel fraction and performance value in each domain; and execute the application in each domain using the number of cores for each domain.