JudoSTM: A Dynamic Binary-Rewriting Approach to Software Transactional Memory
Bibliographic record
Abstract
With the advent of chip-multiprocessors, we are faced with the challenge of parallelizing performance-critical software. Transactional memory (TM) has emerged as a promising programming model allowing programmers to focus on parallelism rather than maintaining correctness and avoiding deadlock. Many implementations of hardware, software, and hybrid support for TM have been proposed; of these, software-only implementations (STMs) are especially compelling since they can be used with current commodity hardware. However, in addition to higher overheads, many existing STM systems are limited to either managed languages or intrusive APIs. Furthermore, transactions in STMs cannot normally contain calls to unobservable code such as shared libraries or system calls. In this paper we present JudoSTM, a novel dynamic binary-rewriting approach to implementing STM that supports C and C++ code. Furthermore, by using value-based conflict detection, JudoSTM additionally supports the transactional execution of both (i) irreversible system calls and (ii) library functions that may contain locks. We significantly lower overhead through several novel optimizations that improve the quality of rewritten code and reduce the cost of conflict detection and buffering. We show that our approach performs comparably to Rochester’s RSTM library-based implementation—demonstrating that a dynamic binary-rewriting approach to implementing STM is an interesting alternative. 1.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".