The use of hardware transactional memory for the trace-based parallelization of recursive Java programs
Bibliographic record
Abstract
We describe a framework for trace-based parallelization of recursive Java programs. We also explore and evaluate the feasibility of using a hardware transactional memory (HTM) system to handle dependences. We design, implement, and evaluate a system that takes as input a sequential program, identifies traces on it, and groups these traces into coarse-grain units of computation, or tasks. We then insert code that allows tasks to execute in parallel transactions using a fork/join paradigm. We also present a software algorithm that ensures sequential program order is maintained by transactional memory. We identify the associated issues and describe criteria that are necessary for Java programs to execute successfully on HTM systems. Our evaluation using JOlden benchmarks indicates that the computational phases of the benchmarks can be executed effectively on HTM systems. The average speedup is 2.7 for four processors. We conclude that HTM is a viable solution to dealing with dependences when performing trace-based parallelization.
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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".