Cache Abstraction for Data Race Detection in Heterogeneous Systems with Non-coherent Accelerators
Why this work is in the frame
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Bibliographic record
Abstract
Embedded systems are becoming increasingly complex and heterogeneous, featuring multiple processor cores (which might themselves be heterogeneous) as well as specialized hardware accelerators, all accessing shared memory. Many accelerators are non-coherent (i.e., do not support hardware cache coherence) because it reduces hardware complexity, cost, and power consumption, while potentially offering superior performance. However, the disadvantage of non-coherence is that the software must explicitly synchronize between accelerators and processors, and this synchronization is notoriously error-prone. We propose an analysis technique to find data races in software for heterogeneous systems that include non-coherent accelerators. Our approach builds on classical results for data race detection, but the challenge turns out to be analyzing cache behavior rather than the behavior of the non-coherent accelerators. Accordingly, our central contribution is a novel, sound (data-race-preserving) abstraction of cache behavior. We prove our abstraction sound, and then to demonstrate the precision of our abstraction, we implement it in a simple dynamic race detector for a system with a processor and a massively parallel accelerator provided by a commercial FPGA-based accelerator vendor. On eleven software examples provided by the vendor, the tool had zero false positives and was able to detect previously unknown data races in two of the 11 examples.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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