Checkpoint/restart in practice: When ‘simple is better’
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 it