Iterative Non-Uniform Profiling for HPC Bottleneck Identification
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Solution Overview
Problem
Current profiling tools for high-performance computing systems, such as gprof, lack the ability to automatically refine bottleneck regions with detailed and diversified metrics, require access to source codes, and fail to differentiate between code regions, leading to inefficient optimization and performance analysis.
Innovation Solution
An iterative, non-uniform profiling method that monitors execution at multiple points, analyzes results, and reconfigures monitoring based on analysis to focus on specific regions of interest, using an information processing system with an instrumentation handler, monitoring handler, analysis handler, and reconfiguration handler to collect and refine performance metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If uniform profiling is applied across the entire code base, then comprehensive coverage is achieved, but time is wasted on uninteresting regions and optimization efficiency deteriorates
Solution Approach 1:
The patent applies non-uniform sampling where different code regions are monitored at different intensities. Hotspots identified in previous iterations receive higher sampling rates and more detailed metrics, while cold regions use lower sampling rates. This local differentiation of monitoring quality resolves the contradiction by concentrating profiling resources where they provide maximum value.
Solution Approach 2:
The patent implements iterative refinement where the profiling strategy dynamically adapts based on previous results. The system transitions from uniform initial sampling to increasingly specialized non-uniform sampling as hotspots are identified. This dynamic adjustment of profiling intensity across iterations resolves the time precision contradiction.
2Measurement precision
If detailed and diversified metrics are collected for all code regions, then bottleneck identification accuracy is improved, but system complexity and overhead increase
Solution Approach 1:
The patent collects detailed and diversified metrics (cache performance, memory access patterns, instruction throughput) only for identified hotspot regions, while using simplified monitoring for other regions. This localized application of comprehensive metric collection maintains high bottleneck identification accuracy without proportionally increasing system complexity.
Solution Approach 2:
The patent applies the principle of partial action by collecting full detailed metrics for only the critical subset of code regions (hotspots) rather than all regions. This partial comprehensive monitoring achieves accurate bottleneck identification while avoiding the excessive complexity of universal detailed monitoring.
3Loss of information
If source code access is required for profiling, then detailed performance metrics can be collected, but proprietary code cannot be analyzed and compilation time increases
Solution Approach 1:
The patent uses binary instrumentation as an intermediary approach, inserting profiling code into the compiled executable without requiring source code access. This intermediary method enables detailed performance metric collection from proprietary binaries while avoiding the need for source code modification or recompilation, thus resolving the contradiction between information completeness and code compatibility.
4Measurement precision
If fine-grained profiling is applied throughout the system, then precise bottleneck location is achieved, but processing overhead and execution time increase
Solution Approach 1:
The patent applies fine-grained profiling locally only to identified hotspot regions rather than uniformly across the entire system. Within hotspots, detailed instrumentation provides precise bottleneck location, while other regions use coarser monitoring. This localized fine-grained approach maintains productivity by limiting overhead to only the critical regions.
Solution Approach 2:
The patent segments the code base into hotspots and non-hotspots based on iterative analysis, applying different profiling granularities to each segment. This segmentation allows fine-grained profiling where needed while maintaining coarse-grained or no profiling elsewhere, thus preserving overall system execution efficiency.
Data Source
AI summary
A method for profiling performance of a system includes steps of: monitoring execution of the system at multiple points during the system's operation; analyzing results derived from the monitoring in order to provide analyzed results; reconfiguring the monitoring non-uniformly according to the analyzed results; and repeatedly performing iterations of the above steps until a particular event occurs. The iterations may be terminated upon: reaching a specified level of analysis precision, determining a source of one or more performance bottlenecks, determining a source of unexpectedly high output or low completion time, completing a predefined number of iterations, reaching an endpoint of an application, or having performed iterations for a specified period of time.