Exploiting wavefront parallelism on large-scale shared-memory multiprocessors
Why this work is in the frame
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Bibliographic record
Abstract
Wavefront parallelism, in which parallelism is limited to hyperplanes in an iteration space, can arise when compilers apply tiling to loop nests to enhance locality. Previous approaches for scheduling wavefront parallelism focused on maximizing parallelism; balancing workloads, and reducing synchronization. In this paper, we show that on large-scale shared-memory multiprocessors, locality is a crucial factor. We make the distinction between intratile and intertile locality and show that as the number of processors grows, intertile locality becomes more important. We consider and experimentally evaluate existing strategies for scheduling wavefront parallelism. We show that dynamic self-scheduling can be efficiently used on a small number of processors, but performs poorly at large scale because it does not enhance intertile locality. By contrast, static scheduling strategies enhance intertile locality for small tiles, maintaining parallelism and resulting in better performance at large scale. Results from a Convex SPP1000 multiprocessor demonstrate the importance of taking intertile locality into account. Static scheduling outperforms 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 | 0.001 | 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