Dynamically dispatching speculative threads to improve sequential execution
Bibliographic record
Abstract
Efficiently utilizing multicore processors to improve their performance potentials demands extracting thread-level parallelism from the applications. Various novel and sophisticated execution models have been proposed to extract thread-level parallelism from sequential programs. One such execution model, Thread-Level Speculation (TLS), allows potentially dependent threads to execute speculatively in parallel. However, TLS execution is inherently unpredictable, and consequently incorrect speculation could degrade performance for the multicore systems. Existing approaches have focused on using the compilers to select sequential program regions to apply TLS. Our research shows that even the state-of-the-art compiler makes suboptimal decisions, due to the unpredictability of TLS execution. Thus, we propose to dynamically optimize TLS performance. This article describes the design, implementation, and evaluation of a runtime thread dispatching mechanism that adjusts the behaviors of speculative threads based on their efficiency. In the proposed system, speculative threads are monitored by hardware-based performance counters and their performance impact is evaluated with a novel methodology that takes into account various unique TLS characteristics. Thread dispatching policies are devised to adjust the behaviors of speculative threads accordingly. With the help of the runtime evaluation, where and how to create speculative threads is better determined. Evaluated with all the SPEC CPU2000 benchmark programs written in C, the dynamic dispatching system outperforms the state-of-the-art compiler-based thread management techniques by 9.4% on average. Comparing to sequential execution, we achieve 1.37X performance improvement on a four-core CMP-based system.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".