Application Performance Spectrometer for Quantized Profiling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for processing large volumes of application performance data in high-performance computing are cumbersome, resource-intensive, and require significant manual effort, often impacting performance and being difficult to interpret for optimization.
Innovation Solution
A system and method utilizing an application performance spectrometer to aggregate, normalize, and quantize performance data, enabling visualization and comparison across different applications, along with machine learning-based predictions for optimizing system configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional performance profiling tools are used to collect comprehensive performance data, then measurement precision is improved, but device complexity increases and productivity decreases due to overwhelming data volumes and processing overhead
Solution Approach 1:
The patent segments the monolithic performance data processing task into multiple independent components: data collection agents running on individual processors, a centralized data receiver, and separate analysis modules. Each processor collects and sends its performance data independently to the receiver, which aggregates and distributes data to relevant users without requiring complex centralized processing of all data points.
Solution Approach 2:
The patent extracts only the essential performance metrics needed for analysis from the complete set of available data. The system identifies and collects specific performance counters relevant to the workload characteristics, filtering out unnecessary data points before transmission and storage, thereby reducing data volume while maintaining measurement precision for critical parameters.
2Measurement precision
If performance monitoring is performed at high frequency with multiple counters, then measurement precision is improved, but productivity worsens due to performance overhead from data collection and processing
Solution Approach 1:
The patent implements partial monitoring by selecting only the necessary performance counters based on workload characteristics rather than collecting all available metrics. The system determines which performance aspects are most relevant to the specific application and focuses monitoring efforts on those areas, performing sufficient but not excessive data collection to maintain precision while minimizing overhead.
Solution Approach 2:
The system employs periodic sampling of performance data at optimized intervals rather than continuous monitoring. The collection frequency is adjusted based on the specific performance parameters being measured and the workload characteristics, performing measurements at regular intervals that provide statistically meaningful data while reducing the total number of data collection operations and associated overhead.
3Measurement precision
If all performance data points are collected and stored, then measurement precision is improved, but loss of time increases due to the difficulty and time required to sort through and interpret vast amounts of data
Solution Approach 1:
The patent implements automated feedback mechanisms where the system continuously monitors performance data and automatically generates alerts and notifications when performance thresholds are exceeded or anomalies are detected. This feedback loop eliminates the need for manual inspection of all data points by providing immediate, actionable information about performance issues, thereby maintaining complete data collection while dramatically reducing interpretation time through automated analysis and communication of results.
Data Source
AI summary
A system and method for generating performance assistance charts is disclosed. An application performance spectrometer aggregates collected application performance data by scaling, normalizing and quantizing the data so that all samples indicative of low performance appear on one side of the graph, all samples indicative of high performance appear on the other side of the graph, and samples in between are positioned relative to those two poles in quantized buckets. The spectrometer may be used to visualize an application's performance characteristics, and as an application fingerprint may be used to compare different applications and determine which have similar performance profiles.