Optimizing process allocation of parallel programs for heterogeneous clusters
Bibliographic record
Abstract
Abstract The performance of a conventional parallel application is often degraded by load‐imbalance on heterogeneous clusters. Although it is simple to invoke multiple processes on fast processing elements to alleviate load‐imbalance, the optimal process allocation is not obvious. Kishimoto and Ichikawa presented performance models for high‐performance Linpack (HPL), with which the sub‐optimal configurations of heterogeneous clusters were actually estimated. Their results on HPL are encouraging, whereas their approach is not yet verified with other applications. This study presents some enhancements of Kishimoto's scheme, which are evaluated with four typical scientific applications: computational fluid dynamics (CFD), finite‐element method (FEM), HPL (linear algebraic system), and fast Fourier transform (FFT). According to our experiments, our new models (NP‐T models) are superior to Kishimoto's models, particularly when the non‐negative least squares method is used for parameter extraction. The average errors of the derived models were 0.2% for the CFD benchmark, 2% for the FEM benchmark, 1% for HPL, and 28% for the FFT benchmark. This study also emphasizes the importance of predictability in clusters, listing practical examples derived from our study. Copyright © 2008 John Wiley & Sons, Ltd.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".