Workload Characterization via Regression and Distance Metrics
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for characterizing computer system workloads are costly and resource-intensive, limiting performance analysis to only a handful of benchmarks, while designers aim to understand a broader range of commercial workloads.
Innovation Solution
A system that collects metrics from computer system workloads, builds a statistical regression model using performance indicators and metrics, and defines a distance metric between workloads to identify representative benchmarks and optimize architectures for customer workloads, leveraging hardware counter metrics and penalized least squares regression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If trace collection is used for workload characterization, then measurement precision is improved, but loss of time and use of energy increase significantly
Solution Approach 1:
The patent extracts only the essential metrics needed for workload characterization from the complete trace data. Instead of collecting and analyzing full traces, the system selectively collects specific hardware counter metrics that capture the essential workload behavior, significantly reducing data volume while maintaining characterization accuracy.
Solution Approach 2:
The patent creates a simplified copy of the workload behavior through statistical regression models that replicate trace-based performance indicators using only hardware counter metrics. This model copy enables workload comparison and benchmark selection without requiring actual trace collection, reducing time and energy costs.
2Measurement precision
If trace collection is performed for multiple workloads, then measurement precision is improved, but use of energy and machine resources increases
Solution Approach 1:
The patent extracts only the essential metrics needed for workload characterization from the complete trace data. Instead of collecting and analyzing full traces, the system selectively collects specific hardware counter metrics that capture the essential workload behavior, significantly reducing data volume while maintaining characterization accuracy.
Solution Approach 2:
The patent replaces expensive, resource-intensive trace collection with cheaper hardware counter metric collection. The hardware counters provide sufficient information for workload characterization at a fraction of the resource cost, making it feasible to analyze many more workloads.
3Device complexity
If a small number of benchmarks are used for performance analysis, then device complexity is reduced, but adaptability to represent diverse commercial workloads decreases
Solution Approach 1:
The patent creates a universal framework using statistical regression models that can characterize and compare any workload using hardware counter metrics. This universal approach allows a small set of benchmarks to effectively represent diverse commercial workloads by measuring their distance in metric space, extending the applicability of limited benchmarks to broader workload scenarios.
Solution Approach 2:
The patent transforms the approach from using many diverse benchmarks to using a small number of benchmarks combined with parameter-based comparison. By changing from workload diversity to parameter space analysis, the system achieves broad workload coverage through mathematical transformation of metric differences into meaningful workload comparisons.
4Loss of time
If hardware counter metrics are used instead of traces, then loss of time is reduced, but measurement precision may decrease
Solution Approach 1:
The patent introduces statistical regression models as intermediaries between hardware counter metrics and workload characterization. These models translate the limited hardware counter data into accurate workload performance predictions by learning the relationship between metrics and trace-based performance indicators, preserving measurement precision while reducing time costs.
Solution Approach 2:
The patent transforms the approach from using many diverse benchmarks to using a small number of benchmarks combined with parameter-based comparison. By changing from workload diversity to parameter space analysis, the system achieves broad workload coverage through mathematical transformation of metric differences into meaningful workload comparisons.
Data Source
AI summary
One embodiment of the present invention provides a system that characterizes computer system workloads. During operation, the system collects metrics for a number of workloads of interest as the workloads of interest execute on a computer system. Next, the system uses the collected metrics to build a statistical regression model, wherein the statistical regression model uses a performance indicator as a response, and uses the metrics as predictors. The system then defines a distance metric between workloads, wherein the distance between two workloads is a function of the differences between metric values for the two workloads. Furthermore, these differences are weighted by corresponding coefficients for the metric values in the statistical regression model.