Cascade: a Collaborative Algorithm for Scalable and Efficient Neighborhood Allgather
Bibliographic record
Abstract
Neighborhood collectives are a critical feature of MPI, enabling efficient communication in applications with sparse communication patterns. This research proposes Cascade, a new algorithm for neighborhood allgather collective that organizes computing nodes along multiple paths based on their distance to the current node. In this approach, messages are forwarded along these paths and propagated until all outgoing neighbors receive them, reducing the communication time. Three performance models are developed to analyze the efficiency of the Cascade algorithm, the default Open MPI algorithm, and the recently proposed Distance-halving neighborhood algorithm in the literature, offering insight into communication cost, scalability, and expected behavior of the algorithms across different system configurations. Experimental results demonstrate that the Cascade algorithm achieves up to 9.54x and 7.05x speedup over Open MPI for random sparse graphs and Moore neighborhoods, respectively. Additionally, the algorithm improves performance by up to$5.25 x$for a sparse matrix-matrix multiplication kernel. The Cascade algorithm outperforms the Distance-halving neighborhood algorithm by up to 2.57 x and 4.81 x speedup for random sparse graphs and Moore neighborhoods, respectively. Moreover, Cascade achieves up to 1.61x performance gain over the Distance-halving neighborhood for the sparse matrix-matrix multiplication kernel. The predictions of our performance models closely match the experimental results.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".