Runtime Empirical Selection of Loop Schedulers on Hyperthreaded SMPs
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
Hyperthreaded (HT) and simultaneous multithreaded (SMT) processors are now available in commodity workstations and servers. This technology is designed to increase throughput by executing multiple concurrent threads on a single physical processor. These multiple threads share the processor's functional units and on-chip memory hierarchy in an attempt to make better use of idle resources. Most OpenMP applications have been written assuming an symmetric multiprocessor (SMP), not an SMT, model. Threads executing on the same physical processor have interactions on data locality and resource sharing that do not occur on traditional SMPs. This work focuses on tuning the behavior of OpenMP applications executing on SMPs with SMT processors. We propose two adaptive loop schedulers that determine effective hierarchical schedulers for individual parallel loops. We compare the performance of our two proposed schedulers against several standard schedulers and the per-region adaptive scheduler proposed by Zhang et al. using the SPEC and NAS OpenMP benchmark suites. We show that both of our proposed schedulers outperform all other schedulers on average, and increase speedup on average by over 25% when all thread contexts are used.
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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