A study of hardware performance monitoring counter selection in power modeling of computing systems
Bibliographic record
Abstract
Power management and energy savings in high-performance computing has become an increasingly important design constraint. The foundation of many power/energy saving methods is based on power consumption models, which commonly rely on hardware performance monitoring counters (PMCs). Various events are provided by processor manufacturers to be monitored using PMCs. PMC event selection has been mainly based on architectural intuitions. However, efficient use of PMCs requires a carefully selected set of events. Therefore, a comprehensive study of PMC events with regards to power modeling is needed to understand and enhance such power models. In this paper, we study the relationship of PMC events with power consumption in the context of single-PMC and multi-PMC power models. Our OpenMP applications are from NAS Parallel Benchmark (BT, CG, LU, and SP) running on an AMD machine. We present the single-PMC selection results for each of our test applications, as well as a unified list for all four applications. Unlike other work that do not consider PMCs as each others' covariates, we present a method to select the most correlated set of PMC events for a given application. Our method finds the desired set of events with 6 times less number of executions compared to a principal component analysis (PCA) method. In addition, we have investigated variability of measurement for correlation coefficients. The 95% confidence interval of power-PMC and PMC-PMC correlation coefficients falls within 1.6% and 2.3% of their measured values, respectively. Furthermore, we study the power and PMC trends in the context of time-series and show that power estimates can be enhanced more than common regression methods. We show that the ARMAX model, a time-series candidate for real-time power estimation, can estimate system power consumption with a mean absolute error (total signal) of 0.1-0.5% in our applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".