Scheduling of wavefront parallelism on scalable shared-memory multiprocessors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Tiling exploits temporal reuse carried by an outer loop of a loop nest to enhance cache locality. Loop skewing is typically required to make tiling legal. This restricts parallelism to wavefronts in the tiled iteration space. For a small number of processors, wavefront parallelism can be efficiently exploited using dynamic self-scheduling with a large tile size. Such a strategy enhances intratile locality, but does not necessarily enhance intertile locality. We show that dynamic self-scheduling performs poorly on scalable shared-memory multiprocessors where smaller tiles are necessary to provide sufficient parallelism-smaller tiles place greater importance on intertile locality. We propose static scheduling strategies which enhance intertile locality for small tiles. Results of experiments on a Convex SPP1000 multiprocessor demonstrate that our strategies outperform dynamic self-scheduling by a factor of up to 2.3 on 30 processors.
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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 | 0.001 | 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