Designing an Offloaded Nonblocking MPI_Allgather Collective Using CORE-Direct
Bibliographic record
Abstract
Collective communication operations in the Message Passing Interface (MPI) consume a significant amount of time at scale, degrading the performance of scientific applications. Optimizing collectives is key to application performance and scalability. This paper focuses on hiding the latency of the allgather collective by efficiently offloading it to the networking hardware. We have investigated the use of Mellanox CORE-Direct offloading technology for independent progression of communication within the collective in order to achieve high communication/computation overlap. This study evaluates several design options for the nonblocking allgather collective and discusses implementations of offloaded Standard Exchange, Ring and Bruck algorithms in flat and hierarchical communicators under single-port and k-port modelling. We have applied our findings to improving the performance of the redesigned Radix Sort application kernel. Performance results suggest that our offloaded nonblocking all gather compares favourably to the blocking variant (with improvements of up to 68% for medium messages in a hierarchical collective) while providing high overlap capability. Multiport modelling is shown to be beneficial, especially in a flat communicator. Radix Sort enjoys up to 40% improvement in its runtime.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".