Application Phase Differentiation for Performance Data Analysis
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Solution Overview
Problem
High-performance computing applications face challenges in efficiently processing large volumes of performance data, which can overwhelm systems and negatively impact performance due to the overhead of traditional profiling methods, making it difficult for developers to extract valuable insights for optimization.
Innovation Solution
The system employs application phase differentiation using deterministic algorithms and machine learning techniques to identify and analyze performance phases, such as CPU-bound, memory-latency bound, and I/O bandwidth bound phases, aggregating and weighting data to provide relative performance insights, enabling better resource allocation and prediction across different computing environments.
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 volume and processing overhead
Solution Approach 1:
The patent segments the application execution into distinct phases (startup, steady-state, shutdown) and collects performance data specific to each phase separately. This segmentation allows the system to focus on collecting meaningful performance data during critical phases while reducing overall data volume and processing complexity.
Solution Approach 2:
The patent extracts and removes unnecessary performance data points that do not contribute to meaningful insights. By filtering out redundant data and focusing only on critical performance metrics during key execution phases, the system reduces data overhead while maintaining measurement precision.
2Measurement precision
If performance monitoring is conducted at high frequency with multiple counters, then measurement precision is improved, but productivity decreases due to significant performance overhead
Solution Approach 1:
The patent implements periodic performance monitoring that adapts the sampling frequency based on the application execution phase. During startup and shutdown phases, monitoring occurs at specific intervals, while during steady-state execution, monitoring frequency is adjusted to balance precision requirements with performance overhead constraints.
Solution Approach 2:
The patent applies partial monitoring by selecting only the most critical performance counters and phases for detailed analysis. Rather than monitoring all possible metrics continuously, the system focuses on essential performance indicators during key execution phases, reducing overhead while maintaining sufficient measurement precision.
3Loss of information
If comprehensive performance data is collected across all application phases, then information completeness is improved, but loss of time increases due to difficulty in sorting and analyzing large data volumes
Solution Approach 1:
The patent segments performance data collection by application phase (startup, steady-state, shutdown), organizing data in a structured manner that facilitates efficient analysis. This segmentation allows developers to quickly locate and analyze performance issues in specific phases without sifting through unrelated data from other phases.
Solution Approach 2:
The patent extracts and retains only the most relevant performance data points from each phase, filtering out redundant information. This extraction process maintains comprehensive coverage of critical performance aspects while reducing the total data volume that requires analysis, thereby minimizing time loss.
4Productivity
If performance monitoring overhead is reduced by increasing sampling interval, then productivity is improved, but measurement precision decreases due to insufficient data points
Solution Approach 1:
The patent implements adaptive periodic sampling that adjusts the monitoring interval based on the application execution phase and performance characteristics. During phases requiring higher precision (such as startup where performance characteristics are being established), sampling frequency increases. During stable phases, the interval increases to reduce overhead, maintaining productivity while preserving measurement precision when needed.
Data Source
AI summary
A system and method for processing application performance using application phase differentiation and detection is disclosed. Phase detection may be accomplished in a number of different ways, including by using a deterministic algorithm that looks for changes in the computing resource utilization patterns (as detected in the performance data collected). Machine learning (ML) and neural networks (e.g. sparse auto encoder SAE) may also be used. Performance data is aggregated according to phase and stored in a database along with additional application and computing system information. This database may then be used to find similar applications for performance prediction.