Hardware Support for Broadcast and Reduce in MPSoC
Bibliographic record
Abstract
MPI has been used as a parallel programming model for supercomputers and clusters but also in Multiprocessor System-on-Chip. One component of MPI is collective communication and its performance is key for parallel applications to achieve good speedups. Considerable research has been done to optimize such communication by improving the MPI library algorithms. However, these optimizations are focused on the processing nodes (end-points in a network) rather than on the network itself. In this paper, we target a Network-on-Chip (NoC) and modify it to provide hardware support for broadcast and reduce operations for the ArchES-MPI library. This library is a subset implementation of the MPI standard targeting embedded processors and hardware accelerators implemented in FPGAs. The experimental results show that for a system with 24 embedded processors, the broadcast and reduce operations improved up to 11.4-fold and 22-fold, respectively. Higher benefits are expected for larger systems at the expense of a modest increase resource utilization.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".