Parallelizing FPGA placement using Transactional Memory
Bibliographic record
Abstract
To capitalize on the growing abundance of multicore hardware, FPGA vendors have begun to parallelize the most compute intensive algorithms in their CAD software. However, parallelization is a painstaking and hence expensive process that limits the number of algorithms that can be cost-effectively parallelized. Transactional Memory (TM) promises an easier-to-use alternative to locks for critical sections in threaded code-allowing programmers to avoid deadlocks and data races, and also allowing critical sections to execute in parallel as long as they dynamically access independent data. In this paper, we present our work on using TM to parallelize simulated annealing-based placement for FPGAs. In particular, we use a software TM (TinySTM) to parallelize the placement phase of Versatile Place and Route (VPR) 5.0.2. With TM we very quickly produced a parallel and correct version of the software, allowing us to focus on incrementally tuning performance. We describe our experiences in tuning the TM system and CAD software, and the interesting algorithmic trade-offs that exist. In the end, we found that optimized transactional placement has the potential for scalable performance: our non-deterministic implementation achieves self-relative speedups over a single thread of 1.82x, 3.62x and 7.27x at 2, 4, and 8 threads respectively with little quality degradation. However, hardware support for TM is likely required to overcome the overheads of STM, as our implementation's single thread performance is 8x slower than sequential VPR.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".