Does faster mean greener? Runtime and energy trade-offs in iOS applications with compiler optimizations
Bibliographic record
Abstract
Smartphones outnumber people nowadays, requiring efficient energy management. High application energy use leads to faster battery drain and frequent recharging, negatively impacting both battery life and the environment. This cycle also contributes to rising electronic and chemical waste due to discarded mobile phone batteries. Compiler optimization flags may play a crucial role in mitigating these issues by optimizing software performance. However, there has been little research on examining how compiler optimization flags impact the energy consumption of smartphone applications. This work presents an empirical study on the effect of the most aggressive iOS compiler optimizations on runtime, power consumption, and energy consumption across six different iOS applications. For each application, we developed a benchmark focused on the specified category we aimed to study. Our results show that reducing application runtime does not always directly correlate with improved energy consumption. In fact, we observed that optimizations aimed at enhancing runtime performance often come at an energy cost in the applications studied, highlighting a trade-off between runtime and energy consumption. For example, we found that using -Ounchecked in Swift, combined with -Oz from LLVM in the GhostRun video game, increases energy consumption by 34%, despite improving runtime performance by 9%.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 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".