SAM: Optimizing Multithreaded Cores for Speculative Parallelism
Bibliographic record
Abstract
This work studies the interplay between multithreaded cores and speculative parallelism (e.g., transactional memory or thread-level speculation). These techniques are often used together, yet they have been developed independently. This disconnect causes major performance pathologies: increasing the number of threads per core adds conflicts and wasted work, and puts pressure on speculative execution resources. These pathologies often squander the benefits of multithreading.We present speculation-aware multithreading (SAM), a simple policy that addresses these pathologies. By coordinating instruction dispatch and conflict resolution priorities, SAM focuses execution resources on work that is more likely to commit, avoiding aborts and using speculation resources more efficiently.We design SAM variants for in-order and out-of-order cores. SAM is cheap to implement and makes multithreaded cores much more beneficial on speculative parallel programs. We evaluate SAM on systems with up to 64 SMT cores. With SAM, 8-threaded cores outperform single-threaded cores by 2.33x on average, while a speculation-oblivious policy yields a 1.85x speedup. SAM also reduces wasted work by 52%.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".