Fractal
Bibliographic record
Abstract
Most systems that support speculative parallelization, like hardware transactional memory (HTM), do not support nested parallelism. This sacrifices substantial parallelism and precludes composing parallel algorithms. And the few HTMs that do support nested parallelism focus on parallelizing at the coarsest (shallowest) levels, incurring large overheads that squander most of their potential. We present FRACTAL, a new execution model that supports unordered and timestamp-ordered nested parallelism. FRACTAL lets programmers seamlessly compose speculative parallel algorithms, and lets the architecture exploit parallelism at all levels. FRACTAL can parallelize a broader range of applications than prior speculative execution models. We design a FRACTAL implementation that extends the Swarm architecture and focuses on parallelizing at the finest (deepest) levels. Our approach sidesteps the issues of nested parallel HTMs and uncovers abundant fine-grain parallelism. As a result, FRACTAL outperforms prior speculative architectures by up to 88x at 256 cores.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.012 |
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".