Barrier Synchronization: Simplified, Generalized, and Solved Without Mutual Exclusion
Bibliographic record
Abstract
Barrier synchronization is a fundamental concurrency issue encountered in a large number of concurrent and parallel applications that involve parallel processes cooperatively solving complex problems. Barrier synchronization essentially forces these parallel processes to wait until each one of them has reached a certain point in execution. Therefore, barrier synchronization has been considered as an inevitable synchronization construct for most parallel applications. Hence barrier synchronization or its variant has become an integral component of the most widely used parallel programming models such as OpenMP, MPI, MapReduce, and the bulk synchronous parallel (BSP) models. A recent work stated that the performance of BSP model depends on 4 parameters - the number of nodes, the speed of each node, the communication cost, and the synchronization cost. Among these, the synchronization cost is considered critical in improving the performance of any BSP implementation. From parallel algorithm design and implementation perspective, we consider that the synchronization component is the most important one and its cost is the most critical performance factor for almost all the parallel programming models including BSP. This paper presents a class of simple barrier synchronization algorithms for shared memory systems. It includes general, efficient, and universal algorithms that are appealing from both practical and theoretical point of view. The correctness of the algorithms are proved. The algorithms are briefly analyzed to expose their strengths and weaknesses.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".