ADEPT scalability predictor in support of adaptive resource allocation
Bibliographic record
Abstract
Adaptive resource allocation with different numbers of machine nodes provides more flexibility and significantly better potential performance for local job and grid scheduling. With the emergence of parallel computing in every-day life on multi-core systems, such schedulers will likely increase in practical relevance. A major reason why adaptive schedulers are not yet practically used is lacking knowledge of the scalability curves of the applications. Existing white-box approaches for scalability prediction are too expensive to apply them routinely. We present ADEPT, a speedup and runtime prediction tool, which is inexpensive and easy-to-use. ADEPT employs a black-box model and can be practically applied at large scale without user or administrator involvement. ADEPT requires neither program analysis and measurements nor user guesses but makes highly accurate predictions with only few observations of application runtime over different numbers of nodes/cores. ADEPT performs efficient model fitting by introducing an envelope-derivation technique to constrain the search. Additionally, ADEPT is capable of handling deviations from the underlying model by detection and automatic correction of anomalies via a fluctuation metric and by considering specific scalability patterns via multi-phase modeling. ADEPT also performs reliability judgment with potential proposal for placement of additional observations. Using MPI and OpenMP implementations of the NAS benchmarks and seven real applications, we demonstrate the effectiveness and high prediction accuracy of ADEPT for both speedup and runtime prediction, including interpolative and extrapolative cases, and show the capability of ADEPT to successfully handle special cases.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".