On metrics for the dynamic load balancing of optimistic simulations
Bibliographic record
Abstract
Focuses on evaluating metrics for use with the dynamic load balancing of optimistic simulations. We present a load balancing algorithm which is token-based and is used in conjunction with clustered time warp (CTW). CTW is a hybrid synchronization protocol, which makes use of a sequential algorithm within clusters of logical processes (LPs) and time warp between the clusters. We define three separate metrics and measure their effectiveness in different simulation environments. One metric measures processor utilization, a second measures the difference in virtual times between the clusters, while a third is a combination of these two metrics. We compare the execution time, memory consumption and throughput obtained in three simulation environments by each of these metrics and to the results obtained without load balancing. Our categories of simulation are VLSI simulations, characterized by a large number of LPs and a low computational granularity; distributed network simulations, in which the workload varies spatially over the execution of the simulation; and a pipeline simulation, characterized by a single direction of message flow. The experiments revealed a significant improvement in the simulation times in the first two categories of simulations when we employed the processor utilization and the combination metrics. For example, improvements of up to 70% were obtained for VLSI simulations. None of the metrics proved to be effective for the pipeline simulation.
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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 | 0.021 | 0.137 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".