Optimal Utilization of Heterogeneous Resources for Biomolecular Simulations

2010 ◽  
Author(s):  
Scott S. Hampton ◽  
Sadaf R. Alam ◽  
Paul S. Crozier ◽  
Pratul K. Agarwal
Keyword(s):  
Biomolecular Simulations ◽  
Heterogeneous Resources

Long-range electrostatic effects in biomolecular simulations

1997 ◽  
Vol 94 ◽  
pp. 1346-1364 ◽  
Author(s):  
TA Darden ◽  
A Toukmaji ◽  
LG Pedersen
Keyword(s):  
Long Range ◽  
Electrostatic Effects ◽  
Biomolecular Simulations

Docking and Biomolecular Simulations on Computer Grids: Status and Trends

2008 ◽  
Vol 4 (3) ◽  
pp. 235-249 ◽  
Author(s):  
Alexandru-Adrian Tantar ◽  
Sebastien Conilleau ◽  
Benjamin Parent ◽  
Nouredine Melab ◽  
Lorraine Brillet ◽  
...  
Keyword(s):  
Status And Trends ◽  
Biomolecular Simulations

EDITORIAL (Hot Topic: Biomolecular Simulations and Applications)

2012 ◽  
Vol 2 (4) ◽  
pp. 313-313
Author(s):  
Athanassios Stavrakoudis
Keyword(s):  
Biomolecular Simulations

EDITORIAL (Hot Topic: Biomolecular Simulations and Applications)

2012 ◽  
Vol 2 (4) ◽  
pp. 313-313
Author(s):  
Athanassios Stavrakoudis
Keyword(s):  
Biomolecular Simulations

Biomolecular Simulations of Metalloenzymes: Strategies to Tackle the Missing Parameter Problem

2012 ◽  
Vol 2 (3) ◽  
pp. 254-262 ◽  
Author(s):  
Alexandra Carvalho
Keyword(s):  
Biomolecular Simulations ◽  
Parameter Problem

scholarly journals Using Open Data to Rapidly Benchmark Biomolecular Simulations: Phospholipid Conformational Dynamics

2021 ◽  
Vol 61 (2) ◽  
pp. 938-949
Author(s):  
Hanne S. Antila ◽  
Tiago M. Ferreira ◽  
O. H. Samuli Ollila ◽  
Markus S. Miettinen
Keyword(s):  
Conformational Dynamics ◽  
Open Data ◽  
Biomolecular Simulations

scholarly journals Minimizing Resource Waste in Heterogeneous Resource Allocation for Data Stream Processing on Clouds

2020 ◽  
Vol 11 (1) ◽  
pp. 149
Author(s):  
Wu-Chun Chung ◽  
Tsung-Lin Wu ◽  
Yi-Hsuan Lee ◽  
Kuo-Chan Huang ◽  
Hung-Chang Hsiao ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Data Stream ◽  
Limited Attention ◽  
Heterogeneous Servers ◽  
Resource Allocation Algorithm ◽  
System Utilization ◽  
Allocation Algorithms ◽  
Considerable Resource ◽  
Heterogeneous Resources

Resource allocation is vital for improving system performance in big data processing. The resource demand for various applications can be heterogeneous in cloud computing. Therefore, a resource gap occurs while some resource capacities are exhausted and other resource capacities on the same server are still available. This phenomenon is more apparent when the computing resources are more heterogeneous. Previous resource-allocation algorithms paid limited attention to this situation. When such an algorithm is applied to a server with heterogeneous resources, resource allocation may result in considerable resource wastage for the available but unused resources. To reduce resource wastage, a resource-allocation algorithm, called the minimizing resource gap (MRG) algorithm, for heterogeneous resources is proposed in this study. In MRG, the gap between resource usages for each server in cloud computing and the resource demands among various applications are considered. When an application is launched, MRG calculates resource usage and allocates resources to the server with the minimized usage gap to reduce the amount of available but unused resources. To demonstrate MRG performance, the MRG algorithm was implemented in Apache Spark. CPU- and memory-intensive applications were applied as benchmarks with different resource demands. Experimental results proved the superiority of the proposed MRG approach for improving the system utilization to reduce the overall completion time by up to 24.7% for heterogeneous servers in cloud computing.

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