Peer Review #2 of "BioWorkbench: a high-performance framework for managing and analyzing bioinformatics experiments (v0.1)"

2018 ◽  
Keyword(s):  
Peer Review ◽  
High Performance ◽  
Performance Framework

scholarly journals Two-Chains: High Performance Framework for Function Injection and Execution

2021 ◽  
Author(s):  
Megan Grodowitz ◽  
Luis E. Pena ◽  
Curtis Dunham ◽  
Dong Zhong ◽  
Pavel Shamis ◽  
...  
Keyword(s):  
High Performance ◽  
Performance Framework

scholarly journals Towards NeuroML: Model Description Methods for Collaborative Modelling in Neuroscience

2001 ◽  
Vol 356 (1412) ◽  
pp. 1209-1228 ◽  
Author(s):  
Nigel H. Goddard ◽  
Michael Hucka ◽  
Fred Howell ◽  
Hugo Cornelis ◽  
Kavita Shankar ◽  
...  
Keyword(s):  
High Performance ◽  
Computational Models ◽  
Object Oriented ◽  
Model Description ◽  
Specific Component ◽  
Nervous Systems ◽  
Efficient Simulation ◽  
Performance Framework ◽  
Structural Levels ◽  
Modelling Process

Biological nervous systems and the mechanisms underlying their operation exhibit astonishing complexity. Computational models of these systems have been correspondingly complex. As these models become ever more sophisticated, they become increasingly difficult to define, comprehend, manage and communicate. Consequently, for scientific understanding of biological nervous systems to progress, it is crucial for modellers to have software tools that support discussion, development and exchange of computational models. We describe methodologies that focus on these tasks, improving the ability of neuroscientists to engage in the modelling process. We report our findings on the requirements for these tools and discuss the use of declarative forms of model description—equivalent to object–oriented classes and database schema—which we call templates. We introduce NeuroML, a mark–up language for the neurosciences which is defined syntactically using templates, and its specific component intended as a common format for communication between modelling–related tools. Finally, we propose a template hierarchy for this modelling component of NeuroML, sufficient for describing models ranging in structural levels from neuron cell membranes to neural networks. These templates support both a framework for user–level interaction with models, and a high–performance framework for efficient simulation of the models.

DroidCollector: A High Performance Framework for High Quality Android Traffic Collection

2016 ◽  
Author(s):  
Dong Cao ◽  
Shanshan Wang ◽  
Qun Li ◽  
Zhenxiang Cheny ◽  
Qiben Yan ◽  
...  
Keyword(s):  
High Performance ◽  
High Quality ◽  
Performance Framework ◽  
Traffic Collection
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