TMD-MPI: An MPI Implementation for Multiple Processors Across Multiple FPGAs
Bibliographic record
Abstract
With current FPGAs, designers can now instantiate several embedded processors, memory units, and a wide variety of IP blocks to build a single-chip, high-performance multiprocessor embedded system. Furthermore, multi-FPGA systems can be built to provide massive parallelism given an efficient programming model. In this paper, we present a lightweight subset implementation of the standard message-passing interface, MPI, that is suitable for embedded processors. It does not require an operating system and uses a small memory footprint. With our MPI implementation (TMD-MPI), we provide a programming model capable of using multiple-FPGAs that hides hardware complexities from the programmer, facilitates the development of parallel code and promotes code portability. To enable intra-FPGA and inter-FPGA communications, a simple network-on-chip is also developed using a low overhead network packet protocol. Together, TMD-MPI and the network provide a homogeneous view of a cluster of embedded processors to the programmer. Performance parameters such as link latency, link bandwidth, and synchronization cost are measured by executing a set of microbenchmarks
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".