Self-hosted placement for massively parallel processor arrays
Bibliographic record
Abstract
We consider the placement problem as part of the CAD flow for a massively parallel processor arrays (MPPAs). In contrast to traditional placers, which operate on a workstation with one or several cores and are able to take advantage of parallelism to a limited degree, we investigate running the placer on the target architecture itself. As the number of processor elements (PEs) in such a device scale, so too does the computational power available to the placer. This natural scaling helps avoid the long runtimes that afflict FPGA flows. In this paper, we propose a distributed placer suitable to run on a MPPA. This placer takes advantage of local interconnect fabric, and may be efficiently coded on a simple, RISC-like core. We investigate the performance of this placer and compare it to traditional, simulated annealing-based placers using both unrealistic (but nearly optimal) and realistic (but suboptimal) annealing schedules. On a simulated 32 × 32 = 1024-core MPPA, the proposed algorithm furnishes placements within 5 % of the optimal placement quality a level competetive with the realistic, traditional placer. To do so, the distributed placer requires each PE to consider 1/256 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> as many swaps as the traditional placer, a computational advantage which scales favourably as the number of cores on the MPPA increases.
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 | 0.000 | 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".