Raising Compute Density of Molecular Dynamics Simulation Through Approximate Memoization
Bibliographic record
Abstract
Molecular dynamics (MD) simulation involves simulating the interactions of particles. MD has many applications in basic biological sciences, drug discovery, materials science, and other fields. Simulating$1\mu \mathrm{s}$of a 100K-atom system can take hours or days11https://www.bdr.riken.jp/en/research/labs/taiji-mlmdgrape4.html, where the compute-heavy aspect of MD is calculating the long-range forces between pairs of particles. In this paper, we explore the application of approximate computing in MD as a means to improve compute density. Specifically, we employ approximate memoization, where previously computed forces (and more) are stored in a table, and are retrieved in subsequent force calculations, provided the inputs to the force calculation are the same or similar. If the prior-computed table values can be used, significant computational work is avoided. In an experimental study, we apply software simulation to understand the degree to which approximation is feasible. We then propose a hardware implementation of memoization to be used within an ASIC MD simulator, MDGRAPE-4A [18]. We show that compute density, measured as$\text{pair-interactions}/(s\cdot\mu m^{2})$is improved substantially, between 40 % and 70 % for the studied cases. This is contingent on the particular system being simulated, the table size, and permitted level of approximation.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".