Data-centric execution of speculative parallel programs
Bibliographic record
Abstract
Multicore systems must exploit locality to scale, scheduling tasks to minimize data movement. While locality-aware parallelism is well studied in non-speculative systems, it has received little attention in speculative systems (e.g., HTM or TLS), which hinders their scalability. We present spatial hints, a technique that leverages program knowledge to reveal and exploit locality in speculative parallel programs. A hint is an abstract integer, given when a speculative task is created, that denotes the data that the task is likely to access. We show it is easy to modify programs to convey locality through hints. We design simple hardware techniques that allow a state-of-the-art, tiled speculative architecture to exploit hints by: (i) running tasks likely to access the same data on the same tile, (ii) serializing tasks likely to conflict, and (iii) balancing tasks across tiles in a locality-aware fashion. We also show that programs can often be restructured to make hints more effective. Together, these techniques make speculative parallelism practical on large-scale systems: at 256 cores, hints achieve near-linear scalability on nine challenging applications, improving performance over hint-oblivious scheduling by 3.3× gmean and by up to 16×. Hints also make speculation far more efficient, reducing wasted work by 6.4× and traffic by 3.5× on average.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".