LAR-CC: Large atomic regions with conditional commits
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
HW/SW Co-designed systems rely on dynamic binary translation and optimizations for efficient execution of binary code. Due to memory ordering properties and other architectural constraints, most binary optimizations are applied to regions of code that are atomically executed. To ensure that the underlying hardware has enough speculative resources to execute the whole atomic region, these systems typically form short atomic regions, with only 20 to 30 instructions. However, the shorter is the atomic region the smaller is the scope for optimizations. We present LAR-CC, a novel technique that enables HW/SW co-designed systems to optimize large atomic regions and dynamically fit them into the available speculative hardware resources by means of conditional commits. The LAR-CC technique consists of two major components: 1) conditional branch instructions to conditionally skip commit operations; 2) code transformations that replace commit operations by conditional commits and enable optimizations to be applied on the large atomic regions. Our experiments show that LAR-CC can effectively achieve dynamic atomic region sizes larger than 1000 instructions, providing sufficiently large scope to apply many advanced optimizations on HW/SW co-designed systems.
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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 it