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 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$5.25 x$</tex> 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".