To checkpoint or not to checkpoint: Understanding energy-performance-I/O tradeoffs in HPC checkpointing
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 two serious design concerns that are expected to become more challenging in future Exascale systems. Therefore, efficiently running systems at such large scales requires an in-depth understanding of the performance and energy costs associated with different fault tolerance techniques. The most commonly used fault tolerance method is checkpoint/restart. Over the years, checkpoint scheduling policies have been traditionally optimized and analysed from a performance perspective. Understanding the energy profile of these policies or how to optimize them for energy savings (rather than performance), remain not very well understood. In this paper, we provide an extensive analysis of the energy/ performance tradeoffs associated with an array of checkpoint scheduling policies, including policies that we propose, as well as few existing ones in the literature. We estimate the energy overhead for a given checkpointing policy, and provide simple formulas to optimize checkpoint scheduling for energy savings, with or without a bound on runtime. We then evaluate and compare the runtime-optimized and energy-optimized versions of the different methods using trace driven simulations based on failure logs from 10 production HPC clusters. Our results show ample room for achieving high energy savings with a low runtime overhead when using non-constant (adaptive) checkpointing methods that exploit characteristics of HPC failures. We also analyze the impact of energy-optimized checkpointing on the storage subsystem, identify policies that are more optimal for I/O savings, and study how to optimize for energy with a bound on I/O time.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".