ArticlePloS one2022
Algorithms for the executable programs planning on supercomputers.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This research dealt with the problem of scheduling applied to the supercomputer's execution. The goal is to develop an appreciated algorithm that schedules a group of several programs characterized by their time consuming very high on different supercomputers searching for an efficient assignment of the total running time. This efficient assignment grantees the fair load distribution of the execution on the supercomputers. The essential goal of this research is to propose several algorithms that can ensure the load balancing of the execution of all programs. In this research, all supercomputers are assumed to have the same hardware characteristics. The main objective is to minimize the gap between the total running time of the supercomputers. This minimization of the gap encompasses the development of novel solutions giving planning of the executable programs. Different algorithms are presented to minimize the gap in running time. The experimental study proves that the developed algorithms are efficient in terms of performance evaluation and running time. A comparison between the presented algorithms is discussed through different classes of instances where in total the number of instances reached 630. The experiments show that the efficient algorithm is the best-programs choice algorithm. Indeed, this algorithm reached the percentage of 72.86%, an average running time of 0.0121, and a gap value of 0.0545.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.