Parallel Control Flow Graph Analysis for Compile-Time App Profiling
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Solution Overview
Problem
Current template metaprogramming techniques for high-performance computing require deep hardware knowledge and lack compile-time performance analysis tools, leading to inefficiencies and issues like race conditions and suboptimal memory usage.
Innovation Solution
A Multi-Environment Template API (META) with a parallel control flow graph (ParCFG) for compile-time analysis of applications, identifying and correcting performance and memory issues using language-agnostic graph analysis techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If detailed application performance data is collected and analyzed, then application performance can be improved, but system overhead and complexity increase
Solution Approach 1:
The patent introduces an application performance manager as an intermediary component that sits between the application and the operating system/kernel. This manager collects performance data, analyzes it, and generates optimization recommendations without requiring direct modifications to the application code or core OS structures. The intermediary handles the complexity of data collection and analysis, isolating it from both the application and the core system, thus improving performance while managing system overhead.
Solution Approach 2:
The patent replaces manual performance tuning and analysis with automated electronic systems. The application performance manager automatically collects metrics, analyzes performance bottlenecks, and generates optimization recommendations without requiring manual intervention. This substitution of mechanical/manual processes with automated electronic analysis reduces system overhead while maintaining detailed performance monitoring capabilities.
2Measurement precision
If performance monitoring is implemented throughout the system, then performance issues can be detected, but processing time and resource consumption increase
Solution Approach 1:
The patent implements selective performance monitoring that focuses on critical performance metrics and bottlenecks rather than monitoring all possible system parameters. The application performance manager identifies and monitors only the most relevant performance indicators for each application, achieving sufficient detection accuracy without the overhead of comprehensive monitoring of every system component and metric.
Solution Approach 2:
The system performs preliminary analysis of performance data to identify patterns and bottlenecks before they become critical issues. By proactively monitoring and analyzing performance metrics in advance, the system can detect potential problems early and generate optimization recommendations before performance degradation occurs, reducing the need for extensive reactive analysis and minimizing processing time.
Data Source
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AI summary
Methods for analyzing and improving a target computer application (182) and corresponding systems (101) and computer-readable mediums (107). A method includes receiving (1602) the target application (182). The method includes generating (1604) a parallel control flow graph (ParCFG) (300, FIG. 9A) corresponding to the target application (182). The method includes analyzing (1606) the ParCFG (300) by the computer system (101). The method includes generating and storing (1610) a modified ParCFG (300, FIG. 9B) for the target application (182).