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 materials. This thesis discusses an algorithm for large-scale MD simulations using modern multiand many-core systems on distributed computing networks. In order to utilize the full processing power of these systems, algorithms must be updated to account for newer hardware, such as the many-core Intel Xeon Phi co-processor. The hybrid method is a data-parallel method of parallelization which combines spatial decomposition using the Message Passing Interface (MPI) to distribute the system onto multiple nodes, along with the cell-task method used for task based parallelism on each node. This allows for the improved performance of task based parallelism on single compute nodes in addition to the benefit of distributed computing allowed by MPI. Results from benchmark simulations on Intel Xeon multi-core processors, and Intel Xeon Phi coprocessors are presented. Results show that the hybrid method provides better performance than either spatial decomposition or cell-task methods alone on single nodes, and that the hybrid method outperforms the spatial decomposition method on multiple nodes, on a variety of system configurations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".