| dc.contributor.author | Cima, Vojtěch | |
| dc.contributor.author | Böhm, Stanislav | |
| dc.contributor.author | Martinovič, Jan | |
| dc.contributor.author | Dvorský, Jiří | |
| dc.contributor.author | Janurová, Kateřina | |
| dc.contributor.author | Vander Aa, Tom | |
| dc.contributor.author | Ashby, Thomas J. | |
| dc.contributor.author | Chupakhin, Vladimir | |
| dc.date.accessioned | 2018-11-30T07:03:38Z | |
| dc.date.available | 2018-11-30T07:03:38Z | |
| dc.date.issued | 2018 | |
| dc.identifier.citation | ACM International Conference Proceeding Series. 2018, p. 1-6. | cs |
| dc.identifier.isbn | 978-1-4503-6444-7 | |
| dc.identifier.uri | http://hdl.handle.net/10084/133294 | |
| dc.description.abstract | Real-world scientific applications often encompass end-to-end data processing pipelines composed of a large number of interconnected computational tasks of various granularity. We introduce HyperLoom, an open source platform for defining and executing such pipelines in distributed environments and providing a Python interface for defining tasks. HyperLoom is a self-contained system that does not use an external scheduler for the actual execution of the task. We have successfully employed HyperLoom for executing chemogenomics pipelines used in pharmaceutic industry for novel drug discovery. | cs |
| dc.format.extent | 194983 bytes | |
| dc.format.mimetype | application/pdf | |
| dc.language.iso | en | cs |
| dc.publisher | Association for Computing Machinery | cs |
| dc.relation.ispartofseries | ACM International Conference Proceeding Series | cs |
| dc.relation.uri | https://doi.org/10.1145/3183767.3183768 | cs |
| dc.rights | © 2018 ACM | cs |
| dc.subject | HPC | cs |
| dc.subject | scientific pipeline | cs |
| dc.subject | machine learning | cs |
| dc.subject | big data | cs |
| dc.subject | distributed computing | cs |
| dc.subject | chemogenomics | cs |
| dc.subject | task scheduling | cs |
| dc.title | HyperLoom: A platform for defining and executing scientific pipelines in distributed environments | cs |
| dc.type | conference paper | cs |
| dc.identifier.doi | 10.1145/3183767.3183768 | |
| dc.relation.projectid | eu-repo/grantAgreement/EU/H2020/671555/ | |
| dc.relation.projectid | eu-repo/grantAgreement/MSMT/LM2015070 | |
| dc.rights.access | openAccess | cs |
| dc.type.version | submittedVersion | cs |
| dc.type.status | Peer-reviewed | cs |
| dc.description.lastpage | 6 | cs |
| dc.description.firstpage | 1 | cs |