Mu-GSIM: A mutation testing simulator on GPUs
Why this work is in the frame
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Bibliographic record
Abstract
Graphics Processing Units (GPUs) have recently gained widespread usage as an advanced parallel platform for accelerating compute intensive applications. The maturity of programming interfaces and the improved programmability of GPUs have enabled the development of parallel algorithms that leverage the wealth of compute power provided by them. In this paper, we present μ-GSIM, a GPU-based simulation tool that leverages the inherent bit parallelism of GPUs for accelerating simulations of mutated digital circuits. We propose an efficient mapping of multiple mutated circuits on the GPU's device memory, where we exploit as much data parallelism as possible so our GPU simulation kernel can achieve maximal performance by operating on independent data. Results show that with the largest ITC'99 circuit benchmarks we were able to achieve a 60% decrease in memory usage while gaining a 5.4× increase in simulation performance. Additionally, we demonstrated a speedup of at least 95× against a commercial event-driven simulation tool running on a conventional processor. This is beneficial in the quest for improving test quality.
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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 | 0.001 |
| 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