Micro-Benchmarking MPI Partitioned Point-to-Point Communication
Bibliographic record
Abstract
Modern High-Performance Computing (HPC) architectures have developed the need for scalable hybrid programming models. The latest Message Passing Interface (MPI) 4.0 standard has introduced a new communication model: MPI Partitioned Point-to-Point communication. This new model allows for the contribution of data from multiple threads with lower overheads than with traditional MPI point-to-point communication. In this paper, we design the first publicly available micro-benchmark suite for MPI Partitioned to measure various metrics that can give insight into the benefits of using this new model and scenarios where MPI point-to-point is better suited. Suggestions are provided to application developers on how to choose partition size for their application based on compute and message size. We evaluate MPI Partitioned communication with both a hot and cold CPU cache, system noise with different probability distributions, point-to-point communication directly, and with commonly used MPI communication patterns such as a halo exchange and Sweep3D.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".