PESC ‐ Parallel Experience for Sequential Code
ABSTRACT The need for computational resources grows as computational algorithms gain popularity in different sectors of the scientific community. Sequential codes need to be converted to parallel versions to optimize the use of these resources. Maintaining a local infrastructure for the execution of...
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| Veröffentlicht in: | Concurrency and computation Jg. 37; H. 12-14 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Hoboken, USA
John Wiley & Sons, Inc
25.06.2025
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| ISSN: | 1532-0626, 1532-0634 |
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| Abstract | ABSTRACT
The need for computational resources grows as computational algorithms gain popularity in different sectors of the scientific community. Sequential codes need to be converted to parallel versions to optimize the use of these resources. Maintaining a local infrastructure for the execution of distributed computing, through desktop grids, for example, has been replaced in favor of cloud platforms that the complexity of these local infrastructures. Unfortunately, the cost of accessing these resources could leave out various studies that could be carried by a simpler infrastructure. In this article, we present a platform for distributing computer simulations on resources available on a local network using container virtualization that s the complexity needed to configure these execution environments and allows any user can benefit from this infrastructure. Simulations could be developed in any programming language (such as Python, Java, C, and R) and with specific execution needs within reach of the scientific community in a general way. We will present results obtained in running simulations that required more than 1000 runs with different initial parameters and various other experiments that benefited from using the platform. |
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| AbstractList | ABSTRACT
The need for computational resources grows as computational algorithms gain popularity in different sectors of the scientific community. Sequential codes need to be converted to parallel versions to optimize the use of these resources. Maintaining a local infrastructure for the execution of distributed computing, through desktop grids, for example, has been replaced in favor of cloud platforms that the complexity of these local infrastructures. Unfortunately, the cost of accessing these resources could leave out various studies that could be carried by a simpler infrastructure. In this article, we present a platform for distributing computer simulations on resources available on a local network using container virtualization that s the complexity needed to configure these execution environments and allows any user can benefit from this infrastructure. Simulations could be developed in any programming language (such as Python, Java, C, and R) and with specific execution needs within reach of the scientific community in a general way. We will present results obtained in running simulations that required more than 1000 runs with different initial parameters and various other experiments that benefited from using the platform. The need for computational resources grows as computational algorithms gain popularity in different sectors of the scientific community. Sequential codes need to be converted to parallel versions to optimize the use of these resources. Maintaining a local infrastructure for the execution of distributed computing, through desktop grids, for example, has been replaced in favor of cloud platforms that abstract the complexity of these local infrastructures. Unfortunately, the cost of accessing these resources could leave out various studies that could be carried by a simpler infrastructure. In this article, we present a platform for distributing computer simulations on resources available on a local network using container virtualization that abstracts the complexity needed to configure these execution environments and allows any user can benefit from this infrastructure. Simulations could be developed in any programming language (such as Python, Java, C, and R) and with specific execution needs within reach of the scientific community in a general way. We will present results obtained in running simulations that required more than 1000 runs with different initial parameters and various other experiments that benefited from using the platform. |
| Author | Souza, Luciano S. Carvalho, Jonathan H. A. Santos, Henrique C. T. Ferreira, Tiago A. E. |
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| Cites_doi | 10.1109/GRID.2004.14 10.1016/j.ins.2022.11.142 10.1109/CLOUD.2019.00073 10.1002/cpe.4770 10.1109/WSC48552.2020.9383971 10.1016/j.jpdc.2021.05.017 10.14569/IJACSA.2017.080117 10.1109/CCGRID.2007.31 10.1002/cpe.4967 10.1007/BF02650179 10.1007/s10723-018-09472-w 10.1109/TPDS.2018.2850004 10.1007/978-3-030-61377-8_9 10.1145/3459665 10.1016/j.jpdc.2020.08.006 10.1007/978-3-030-91702-9_17 10.1016/j.tele.2018.10.007 |
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The need for computational resources grows as computational algorithms gain popularity in different sectors of the scientific community. Sequential... The need for computational resources grows as computational algorithms gain popularity in different sectors of the scientific community. Sequential codes need... |
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| SubjectTerms | container virtualization desktop grid distributed computing parallel programing sequential code |
| Title | PESC ‐ Parallel Experience for Sequential Code |
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