A parallel programming model for a multi-FPGA multiprocessor machine
Bibliographic record
Abstract
Recent research has shown that FPGAs can execute certain applications significantly faster than state-of-the-art processors. The penalty is the loss of generality, but the reconfigurability of FPGAs allows them to be reprogrammed for other applications. Therefore, an efficient programming model and a flexible design flow are paramount for FPGA technology to be more widely accepted. In this thesis, a lightweight subset implementation of the MPI standard, called TMD-MPI, is presented. TMD-MPI provides a programming model capable of using multiple-FPGAs and embedded processors while hiding hardware complexities from the programmer, facilitating the development of parallel code and promoting code portability. A message-passing engine (TMD-MPE) is also developed to encapsulate the TMD-MPI functionality in hardware. TMD-MPE enables the communication between hardware engines and embedded processors. In addition, a Network-on-Chip is designed to enable intra-FPGA and inter-FPGA communications. Together, TMD-MPI, TMD-MPE and the network provide a flexible design flow for Multiprocessor System-on-Chip design.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".