Efficient External Sorting for Memory-Constrained Embedded Devices with Flash Memory
Bibliographic record
Abstract
Embedded devices are ubiquitous in areas of industrial and environmental monitoring, health and safety, and consumer appliances. A common use case is data collection, processing, and performing actions based on data analysis. Although many Internet of Things (IoT) applications use the embedded device simply for data collection, there are benefits to having more data processing done closer to data collection to reduce network transmissions and power usage and provide faster response. This work implements and evaluates algorithms for sorting data on embedded devices with specific focus on the smallest memory devices. In devices with less than 4 KB of available RAM, the standard external merge sort algorithm has limited application as it requires a minimum of three memory buffers and is not flash-aware. The contribution is a memory-optimized external sorting algorithm called no output buffer sort (NOBsort) that reduces the minimum memory required for sorting, has excellent performance for sorted or near-sorted data, and sorts on external memory such as SD cards or raw flash chips. When sorting large datasets, no output buffer sort reduces I/O and execution time by between 20% to 35% compared to standard external merge sort.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".