Moleclar-dynamics simulations using spatial decomposition and task-based parallelism
Bibliographic record
Abstract
Molecular Dynamics (MD) simulations are an integral method in the computational studies of \nmaterials. This thesis discusses an algorithm for large-scale MD simulations using modern multiand \nmany-core systems on distributed computing networks. In order to utilize the full processing \npower of these systems, algorithms must be updated to account for newer hardware, such as the \nmany-core Intel Xeon Phi co-processor. \nThe hybrid method is a data-parallel method of parallelization which combines spatial decomposition \nusing the Message Passing Interface (MPI) to distribute the system onto multiple nodes, \nalong with the cell-task method used for task based parallelism on each node. This allows for the \nimproved performance of task based parallelism on single compute nodes in addition to the benefit \nof distributed computing allowed by MPI. \nResults from benchmark simulations on Intel Xeon multi-core processors, and Intel Xeon Phi \ncoprocessors are presented. Results show that the hybrid method provides better performance \nthan either spatial decomposition or cell-task methods alone on single nodes, and that the hybrid \nmethod outperforms the spatial decomposition method on multiple nodes, on a variety of system \nconfigurations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".