Reducing memory buffering overhead in software thread-level speculation
Bibliographic record
Abstract
Software-based, automatic parallelization through Thread-Level Speculation (TLS) has significant practical potential, but also high overhead costs. Traditional "lazy" buffering mechanisms enable strong isolation of speculative threads, but imply large memory overheads, while more recent "eager" mechanisms improve scalability, but are more sensitive to data dependencies and have higher rollback costs. We here describe an integrated system that incorporates the best of both designs, automatically selecting the best buffering mechanism. Our approach builds on well-optimized designs for both techniques, and we describe specific optimizations that improve both lazy and eager buffer management as well. We implement our design within MUTLS, a software-TLS system based on the LLVM compiler framework. Results show that we can get 75% geometric mean performance of OpenMP versions on 9 memory intensive benchmarks. Application of these optimizations is thus a useful part of the optimization stack needed for effective and practical software TLS.
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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".