Quantum transport modeling with GPUs.
Bibliographic record
Abstract
In this thesis, we have developed a parallel GPU accelerated code for carrying out transport calculations within the Non-Equilibrium Green's Function (NEGF) framework using the Tight-Binding (TB) model.We also discuss the theoretical, modelling, and computational issues that arise in this implementation.We demonstrate that a heterogenous implementation with CPUs and GPUs is superior to single processor, multiple processor, and massively parallel CPU-only implementations.The GPU-Matlab Interface (GMI) developed in this work for use in our NEGF-TB code is not application specific and can be used by researchers in any field without previous knowledge of GPU programming or multi-threaded programming.We also demonstrate that GMI competes very well with commercial packages.Finally, we apply our heterogenous NEGF-TB code to the study of electronic transport properties of Si nanowires and nanobeams.We investigate the eect of several kinds of structural defects on the conductance of such devices and demonstrate that our method can handle systems of over 200,000 atoms in a reasonable time scale while using just 1-4 GPUs.vii
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".