Does faster mean greener? Runtime and energy trade-offs in iOS applications with compiler optimizations
Why this work is in the frame
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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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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it