Adaptive Performance Data Collection for HPC Systems
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Solution Overview
Problem
High-performance computing systems face challenges in efficiently collecting and processing performance data due to overwhelming data volumes, which negatively impact system performance and require significant time and expertise to analyze, making it difficult to strike a balance between data resolution and performance impact.
Innovation Solution
A lightweight, real-time performance data collection system that aggregates data into databases, adjusts sampling intervals and counters based on feedback from modules like system health and security monitors, and uses a FIFO buffer to manage storage, allowing for real-time analysis and recommendations for optimization.
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 loss of time increases
Solution Approach 1:
The patent extracts only the most relevant performance counters and metrics needed for HPC workload analysis, rather than collecting all available performance data. This selective extraction approach reduces the volume of data generated and processed, thereby decreasing system overhead and complexity while maintaining sufficient measurement precision for identifying performance bottlenecks in HPC applications.
Solution Approach 2:
The patent applies different collection strategies to different performance metrics based on their importance and characteristics. Critical metrics are collected with higher resolution and frequency, while less important metrics are collected at lower resolution. This localized quality approach ensures that measurement precision is maintained for key performance indicators while reducing overall system overhead.
2Measurement precision
If performance data is collected at high resolution with frequent sampling, then measurement precision is improved, but productivity decreases due to performance impact
Solution Approach 1:
The patent implements periodic sampling of performance counters at strategically selected intervals rather than continuous monitoring. By sampling at periodic intervals that are optimized for the specific HPC workload and application characteristics, the system maintains sufficient measurement precision to capture performance trends and bottlenecks while minimizing the frequency of sampling operations that would impact application performance and productivity.
Solution Approach 2:
The patent collects performance data at a resolution that is partially sufficient for most analysis needs, rather than attempting to collect all possible data at maximum resolution. This partial action approach focuses resources on collecting the most critical performance metrics at appropriate resolutions, thereby maintaining measurement precision for key insights while avoiding the excessive data collection that would significantly impact application performance.
3Measurement precision
If all performance counters are monitored continuously, then measurement precision is improved, but loss of time increases for data processing and analysis
Solution Approach 1:
The patent extracts and focuses on a curated subset of performance counters that are most relevant to HPC workload analysis, excluding less relevant metrics from continuous monitoring. This extraction strategy reduces the total number of counters that need to be processed and analyzed, thereby decreasing data processing time while maintaining measurement precision for the most important performance indicators that drive HPC optimization decisions.
4Productivity
If performance monitoring overhead is reduced by sampling less frequently, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The patent applies differentiated sampling strategies to different performance metrics based on their importance and temporal characteristics. Critical performance counters that directly impact HPC application performance are sampled more frequently with higher precision, while less critical metrics are sampled at lower frequencies. This local quality approach ensures that measurement precision is maintained for the metrics that matter most to productivity, while allowing reduced sampling for secondary metrics.
Solution Approach 2:
The patent implements dynamic sampling intervals that adapt based on system state and workload characteristics. During periods of significant performance changes or when bottlenecks are detected, sampling frequency increases automatically to maintain measurement precision. During stable operating conditions, sampling frequency decreases to minimize overhead. This dynamic approach balances measurement precision and productivity by adjusting the monitoring intensity to match actual system needs.
Data Source
AI summary
Systems and methods for collecting performance data in high performance computing systems are disclosed. To prevent massive amounts of performance data from overwhelming the system and negatively impacting performance, collected performance data may be processed into two databases: (i) an aggregate database, and (ii) a time-series database holding the newest information for real time performance analysis. Storage space may be saved by using a FIFO buffer to store collected performance data. A real-time performance collection engine may adjust the performance sampling interval used and the particular performance counters used based on measured system impact and feedback from other system modules consuming the performance data.