Bibliographic record
Abstract
MPI (Message Passing Interface) has been successfully used in the high performance computing community for years and is the dominant programming model. Current implementations of MPI are coarse-grained, with a single MPI process per processor, however, there is nothing in the MPI specification precluding a finer-grain interpretation of the standard. We have implemented Fine-grain MPI (FG-MPI), a system that allows execution of hundreds and thousands of MPI processes on-chip or communicating between chips inside a cluster. FG-MPI uses fibers (coroutines) to support multiple MPI processes inside an operating system process. These are fullfledged MPI processes each with their own MPI rank. We have implemented a fine-grain version of MPICH2 middleware that uses the Nemesis communication subsystem for intranode and internode communication. We present experimental results for a real-world application that uses thousands of MPI processes and compare its performance with the following fine-grain multicore languages: Erlang, Haskell, Occam-pi and POSIX threads. Our results show that FG-MPI scales well and outperforms many of these other programming languages used for parallel programming on multicore systems while retaining MPI's intranode and internode communication abilities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".