Design and Implementation of MPI-Native GPU-Initiated MPI Partitioned Communication
Bibliographic record
Abstract
Graphics Processing Units have become the dominant type of accelerators for high-performance computing and artificial intelligence. To support these systems, new communication libraries have emerged, such as NCCL, RCCL, and NVSHMEM, providing stream-based semantics and GPU-Initiated Communication. Some of the best performing communication libraries are unfortunately vendor-specific, and may use load-store semantics that have been traditionally underused in the application community. Moreover, the Message Passing Interface (MPI) has yet to define explicit GPU support mechanisms, making it difficult to deploy the message-passing communication model efficiently on GPU-based systems. However, MPI-4.0 introduced MPI Partitioned Point-to-Point communication, which facilitates hybrid-programming models. For example, Partitioned Communication is designed to allow GPUs to trigger data movement through a persistent intra- or inter-node channel. In this work, we extend MPI Partitioned to provide Intra-Kernel GPU-Initiated Communication and Partitioned Collectives, augmenting MPI with techniques used in vendor specific libraries. We evaluate our designs on a NVIDIA GH200 Grace Hopper Superchip testbed, to understand the benefits of GPU-Initiated communication on NVLink and InfiniBand networks. We assess the benefits at the application layer using a Jacobi solver and Partitioned Allreduce with Deep Learning Kernels.
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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 | 0.000 |
| 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".