Multi-Pivot Quicksort: Theory and Experiments
Why this work is in the frame
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Bibliographic record
Abstract
The idea of multi-pivot quicksort has recently received the attention of researchers after Vladimir Yaroslavskiy proposed a dual pivot quicksort algorithm that, contrary to prior intuition, outperforms standard quicksort by a a significant margin under the Java JVM [10]. More recently, this algorithm has been analysed in terms of comparisons and swaps by Wild and Nebel [9]. Our contributions to the topic are as follows. First, we perform the previous experiments using a native C implementation thus removing potential extraneous effects of the JVM. Second, we provide analyses on cache behavior of these algorithms. We then provide strong evidence that cache behavior is causing most of the performance differences in these algorithms. Additionally, we build upon prior work in multi-pivot quicksort and propose a 3-pivot variant that performs very well in theory and practice. We show that it makes fewer comparisons and has better cache behavior than the dual pivot quicksort in the expected case. We validate this with experimental results, showing a 7–8% performance improvement in our tests.
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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