Communication‐aware message matching in MPI
Bibliographic record
Abstract
Summary The Message Passing Interface (MPI) is the de facto standard for parallel programming in High Performance Computing (HPC). Asynchronous communications in MPI involve message matching semantics that must be satisfied by the conforming libraries. The matching performance is in the critical path of communications in MPI. However, the current message matching approaches suffer from scalability issues and/or do not consider the message queue characteristics of the applications. In this paper, we propose a new message matching mechanism for MPI that can speed up the operation by allocating dedicated queues for certain communications of an application. More specifically, we propose a design that categorizes communications into a set of partners and non‐partners based on the communication frequency in the corresponding queues. We propose a static and a dynamic approach for our message matching design. While the static approach works based on the information from a profiling stage, the dynamic approach utilizes the message queue characteristics at runtime. Our experimental evaluations show that the proposed design can provide up to 28x speedup in queue search time for long list traversals without degrading the performance for short list traversals. We can also gain up to 5x speedup for the FDS application, which is highly affected by the message matching performance.
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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 | 0.002 | 0.006 |
| 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 | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".