Dual-mode processor pipeline sampling for idle time utilization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Contemporary pipeline sampling techniques are inefficient as they often result in a significant portion of sampled data being idle or inactive, leading to wasted sampling time and limited usable data for analysis, as only a subset of sampling time generates usable information when the pipeline is active.
Innovation Solution
Implementing a dual or multi-mode sampling system that gathers alternative data when the pipeline is idle, including overall system information relevant to pipeline performance but not dependent on pipeline activity, allowing for confirmation of system performance characteristics and analysis of additional characteristics that cannot be inferred from active pipeline data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If sampling is performed at periodic intervals regardless of pipeline activity, then sampling time is utilized consistently, but a significant portion of sampled data becomes idle or inactive when the pipeline is idle
Solution Approach 1:
The sampling system dynamically adapts its behavior based on pipeline activity state. When the pipeline is active, the system samples pipeline data; when idle, it samples alternative system information. This dynamic adaptation resolves the contradiction by making sampling time productive regardless of pipeline state, eliminating wasted sampling opportunities while maintaining data quality.
Solution Approach 2:
The system changes the parameter being sampled based on pipeline activity. Instead of always sampling pipeline data, the system switches between sampling pipeline data and alternative system information depending on the pipeline's operational state. This parameter change ensures that every sampling interval produces usable information, addressing both time utilization and data quality concerns.
2Productivity
If sampling time is kept within set limits to conserve processor resources, then processor overhead is controlled, but the amount of usable data collected is reduced
Solution Approach 1:
The sampling system is designed to perform multiple functions within the same sampling infrastructure. By sampling both pipeline data and alternative system information (such as system bus activity, cache controller activity, or other processor components) using the same sampling circuitry and time intervals, the system maximizes data collection without increasing processor overhead or extending sampling time limits.
3Device complexity
If only pipeline data is sampled, then sampling focus remains narrow and processing is simplified, but comprehensive system performance characteristics cannot be analyzed
Solution Approach 1:
The system introduces alternative system information as an intermediary data source that complements pipeline data. This intermediary information (such as system bus activity, cache controller status, or memory controller data) provides additional performance insights without requiring a complete redesign of the sampling system. The same sampling infrastructure that collects pipeline data also collects this intermediary information, achieving comprehensive analysis with minimal added complexity.
Data Source
AI summary
Embodiments are directed to systems and methodologies for efficiently sampling data for analysis by a pipeline analysis algorithm. The amount of sampled data is maximized without increasing sampling overhead by sampling “non-pipeline activity” data if the subject pipeline is inactive during the sampling time. The non-pipeline activity data is selected to include overall system information that is relevant to the subject pipeline's performance but is not necessarily dependent on whether the subject pipeline is active. In some embodiments, the non-pipeline activity data allows for confirmation of a pipeline performance characteristic that must otherwise be inferred by the subsequent pipeline analysis algorithm from data sampled while the pipeline was active. In some embodiments, the non-pipeline activity data allows the pipeline analysis algorithm to analyze additional performance characteristics that cannot otherwise be inferred from the data sampled while the pipeline was active.