FPGA-based supercomputing
Bibliographic record
Abstract
Current high-performance supercomputing applications are typically implemented on large-scale general-purpose distributed or multiprocessing systems often based on commodity microprocessors. FPGAs have now reached a level of sophistication that they too could be used for such applications. We explore the feasibility of using FPGAs to implement large-scale application-specific computations by way of a case study that implements a novel Molecular Dynamics system. The system has been designed such that it is scalable and parallelizable. On the Transmogrifier 3, the system performs calculations on an 8,192 particle system in 37 seconds at 26MHz. This implementation shows that by scaling to more modern parts running at 100MHz and using a better architecture, a speedup of over 20x can be achieved compared to a state-of-the-art microprocessor. This can also be achieved at less cost, using less power and taking less space than a standard microprocessor-based system, while maintaining the computational precision required.
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 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 | 0.001 | 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".