Dynamic, Hardware-Conscious Tuning of Concurrent Multithreaded Analytical Database Queries
Bibliographic record
Abstract
This thesis introduces a framework called PCMonitor that monitors underlying hardware metrics during a program’s execution and enables applications to make dynamic, runtime changes in order to achieve better performance. We apply PCMonitor to a custom hash join implementation in order to emulate main-memory database systems and show the performance improvement of concurrently running queries. In modern databases, optimizations in query planning and resource allocation happen before a query executes, thereby disregarding the runtime hardware conditions that are often different than what can be predicted during the optimization process. PCMonitor makes use of performance counter events and memory profiling in order to monitor hardware conditions continuously as a query is executing and then algorithmically makes decisions about certain actions and re-configurations that the query can take in order to improve performance. This takes traditional query optimization one step further by creating elastically adapting queries that act based on real- time conditions. We show that by making runtime decisions using PCMonitor, concurrently running queries can make better decisions about pausing and resuming their execution, sharing memory resources and sharing hardware resources, resulting in up to 50% better performance for individual queries.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".