Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 226

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

A fast, scalable method for the parallel evaluation of distance‐limited pairwise particle interactions

Shaw, David E.·2005·Journal of Computational Chemistry

Abstract

Classical molecular dynamics simulations of biological macromolecules in explicitly modeled solvent typically require the evaluation of interactions between all pairs of atoms separated by no more than some distance R, with more distant interactions handled using some less expensive method. Performing such simulations for periods on the order of a millisecond is likely to require the use of massive parallelism. The extent to which such simulations can be efficiently parallelized, however, has historically been limited by the time required for interprocessor communication. This article introduces a new method for the parallel evaluation of distance‐limited pairwise particle interactions that significantly reduces the amount of data transferred between processors by comparison with traditional methods. Specifically, the amount of data transferred into and out of a given processor scales as O(R3/2p−1/2), where p is the number of processors, and with constant factors that should yield a substantial performance advantage in practice. © 2005 Wiley Periodicals, Inc. J Comput Chem 26: 1318–1328, 2005

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1002/jcc.20267

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 1 references

Cited by

See all 226

Metadata

Authors
Shaw, D. E.
Venue
Journal of Computational Chemistry
Publisher
Wiley
Published
2005-07-12
Volume
26
Issue
13
Pages
1318-1328
ISSN
  • 0192-8651 (ISSN-L)
  • 1096-987X
OpenAlex
W2144803600
Cited by
226
References
1
FWCI
11.07
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: A fast, scalable method for the parallel evaluation of distance‐limited pairwise particle interactions. https://app.cochat.ai/papers/10.1002/jcc.20267
The paper
Shaw, D. E. (2005). A fast, scalable method for the parallel evaluation of distance‐limited pairwise particle interactions. Journal of Computational Chemistry. https://doi.org/10.1002/jcc.20267

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.