Using SIMD registers and instructions to enable instruction-level parallelism in sorting algorithms
Bibliographic record
Abstract
Most contemporary processors offer some version of Single Instruction Multiple Data (SIMD) machinery — vector registers and instructions to manipulate data stored in such registers. The central idea of this paper is to use these SIMD resources to improve the performance of the tail of recursive sorting algorithms. When the number of elements to be sorted reaches a set threshold, data is loaded into the vector registers, manipulated in-register, and the result stored back to memory. Three implementations of sorting with two different SIMD machineries — x86-64’s SSE2 and G5’s AltiVec — demonstrate that this idea delivers significant speed improvements. The improvements provided are orthogonal to the gains obtained through empirical search for a suitable sorting algorithm [11]. When integrated with the Dynamically Tuned Sorting Library (DTSL) this new code generation strategy reduces the time spent by DTSL up to 22 % for moderately-sized arrays, with greater relative reductions for small arrays. Wall-clock performance of d-heaps is improved by up to 39 % using a similar technique.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".