Checkpoint/restart in practice: When ‘simple is better’
Bibliographic record
Abstract
Efficient use of high-performance computing (HPC) installations critically relies on effective methods for fault tolerance. The most commonly used method is checkpoint/restart, where an application writes periodic checkpoints of its state to stable storage that it can restart from in the case of a failure. Despite the prevalence of checkpoint/restart, it is still not very well understood in practice how to set its key parameter, the checkpoint interval. Despite a large body of theoretical work, practitioners still rely on crude rules-of-thumb such as “checkpoint once every hour”. Our goal is to identify methods for optimizing the checkpointing process that are easy to use in practice and at the same time achieve high quality solutions. In particular, our paper makes the following contributions: We evaluate an array of methods for optimizing the checkpoint interval, some previously known as well as new ones that we propose, using real-world failure logs. We show that a very simple closed-form solution can easily be adapted for use in practice and achieves near-optimal performance. We also find that more complex solutions only negligibly improve performance based on real world traces. We show that simple back-of-the envelope formulas can be used to accurately estimate the wasted work in HPC systems, and make projections of future HPC systems and requirements for their efficient use.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".