Hierarchical schedulings of time-next-event heuristic on distributed memory machines
Bibliographic record
Abstract
The time-next-event (TNE) algorithm relies upon a shortest path algorithm which is independently executed on each processor in order to unblock the logical processes (LPs) in a processor and to increase the parallelism of the simulation. TNE's performance is good, and it is able to produce better lookahead as a consequence of obtaining input from LPs resident on neighboring processors. This paper presents an extension of the TNE algorithm, the objective of which is to increase its parallelism and to break the interprocessor deadlocks inherent with the use of TNE. This semi-global TNE (SGTNE) algorithm is executed over a cluster of processors as opposed to TNE, which is executed over a cluster of processes assigned to a single processor. Because TNE is executed on individual processors, it is susceptible to interprocessor deadlocks. These deadlocks must be detected and broken at some cost. SGTNE helps to break these deadlocks by executing a shortest path algorithm over a snapshot of the LPs in a cluster of processors. We examine two (hierarchical) scheduling algorithms for SGTNE which we refer to as one- and two-level scheduling in the context of a queuing network simulation of a torus. The torus was chosen because its many cycles aid in the formation of deadlock. In two-level scheduling, we execute TNE on each individual processor before calling the global portion of the algorithm. Our experiments indicate that two-level scheduling is superior to one-level scheduling. They also indicate that SGTNE decreases the run time of a TNE simulation by about 15-20% when a large number of processors is used.>
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".