Mitigating synchronization bottlenecks in high-performance actor-model-based software
Bibliographic record
Abstract
Bulk synchronous programming (in distributed-memory systems) and the fork-join pattern (in shared-memory systems) are often used for problems where independent processes must periodically synchronize. Frequent synchronization can greatly undermine the performance of software designed to solve such problems. We use the actor model of concurrent computing to balance the load of hundreds of thousands of short-lived tasks and mitigate synchronization bottlenecks by buffering communication via actor batching. The actor model is becoming increasingly popular in scientific and high-performance computing because it can handle heterogeneous tasks and computing environments with enhanced programming flexibility and ease relative to conventional paradigms like MPI. For a hydrologic simulation of continental North America with over 500,000 elements, the proposed buffering approach is approximately 4 times faster than no buffering, outperforms MPI on single and multiple nodes, and remains competitive with OpenMP on a single node and MPI+OpenMP on multiple nodes.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".