Understanding Practical Tradeoffs in HPC Checkpoint-Scheduling Policies
Bibliographic record
Abstract
As the scale of High-Performance Computing (HPC) clusters continues to grow, their increasing failure rates and energy consumption levels are emerging as serious design concerns. Efficiently running systems at such large scales critically relies on deploying effective, practical methods for fault tolerance while having a good understanding of their respective performance and energy overheads. The most commonly used fault tolerance method is checkpoint/restart. Checkpoint scheduling policies, however, have been traditionally optimized and analysed from one angle: application performance. In this work, we provide an extensive analysis of the performance, energy and I/O costs associated with a wide array of checkpointing policies. We consider practical deployment issues and show that simple formulas can be used to accurately estimate wasted work in a system. We propose methods to optimize checkpoint scheduling for energy savings and evaluate the runtime-optimized and energy-optimized policies using simulations based on failure logs from 10 production HPC clusters. Our results show ample room for achieving high quality energy/performance tradeoffs when using methods that exploit characteristics of real world failures. We also analyze the impact of energy-optimized checkpointing on the storage subsystem and identify policies that are optimal for I/O savings.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".