The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Dask: Parallel Computation with Blocked algorithms and Task Scheduling
Open accessAbstract
Dask enables parallel and out-of-core computation.We couple blocked algorithms with dynamic and memory aware task scheduling to achieve a parallel and out-of-core NumPy clone.We show how this extends the effective scale of modern hardware to larger datasets and discuss how these ideas can be more broadly applied to other parallel collections.
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.
Integrity signals
Registry
Cites
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.
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
Python for scientific computing
Oliphant, T. E. (2007) · Computing in Science & Engineering · doi:10.1109/mcse.2007.58 · Cited by 3,303
Cited by
CellProfiler 4: improvements in speed, utility and usability
Stirling, D. R., Swain-Bowden, M. J., Lucas, A. M., Carpenter, A. E., Cimini, B. A., & Goodman, A. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04344-9 · Cited by 2,070
xarray: N-D labeled Arrays and Datasets in Python
Hoyer, S., & Hamman, J. (2017) · Journal of Open Research Software · doi:10.5334/jors.148 · Cited by 1,524
Time Series FeatuRe Extraction on basis of Scalable Hypothesis tests (tsfresh – A Python package)
Christ, M., Braun, N., Neuffer, J., & Kempa-Liehr, A. W. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.03.067 · Cited by 1,257
The SunPy Project: Open Source Development and Status of the Version 1.0 Core Package
Community, T. S., Barnes, W. T., Bobra, M. G., Christe, S. D., Freij, N., Hayes, L. A., Ireland, J., Mumford, S., Perez-Suarez, D., Ryan, D. F., et al. (2020) · The Astrophysical Journal · doi:10.3847/1538-4357/ab4f7a · Cited by 494
The scverse project provides a computational ecosystem for single-cell omics data analysis
Virshup, I., Bredikhin, D., Heumos, L., Palla, G., Sturm, G., Gayoso, A., Kats, I., Koutrouli, M., Community, S., Angerer, P., et al. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01733-8 · Cited by 341
Metadata
- Authors
- Rocklin, M.
- Venue
- Proceedings of the Python in Science Conference
- Publisher
- SciPy
- Published
- 2015
- Pages
- 126-132
- ISSN
- 2575-9752 (ISSN-L)
- OpenAlex
- W2527672088
- Cited by
- 801
- References
- 1
- FWCI
- 8.90
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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: Dask: Parallel Computation with Blocked algorithms and Task Scheduling. https://app.cochat.ai/papers/10.25080/majora-7b98e3ed-013
- The paper
- Rocklin, M. (2015). Dask: Parallel Computation with Blocked algorithms and Task Scheduling. Proceedings of the Python in Science Conference. https://doi.org/10.25080/majora-7b98e3ed-013
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.
