Efficient Data Encoding for Improving Fault Simulation Performance on GPUs
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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. One of the key factors for achieving maximal performance of GPU kernels is to ensure that the data used for computation is mapped independently. This implies that data duplication is needed, however, an efficient mapping of data is imperative as the on-board memory capacities on GPUs are limited. In this paper, we present a novel data encoding technique for creating an efficient data mapping for the application of fault simulation algorithms. Fault simulation requires a certain level of data dependency which creates a challenge for efficiently mapping the circuit data on the GPU's device memory. Based on our results, our memory optimization techniques were able to reduce the memory usage by 80% with speed ups reaching over 60× in the circuit benchmarks tested.
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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 | 0.001 | 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