2019 ◽  
Vol 12 (7) ◽  
pp. 3001-3015 ◽  
Author(s):  
Shahbaz Memon ◽  
Dorothée Vallot ◽  
Thomas Zwinger ◽  
Jan Åström ◽  
Helmut Neukirchen ◽  
...  

Abstract. Scientific computing applications involving complex simulations and data-intensive processing are often composed of multiple tasks forming a workflow of computing jobs. Scientific communities running such applications on computing resources often find it cumbersome to manage and monitor the execution of these tasks and their associated data. These workflow implementations usually add overhead by introducing unnecessary input/output (I/O) for coupling the models and can lead to sub-optimal CPU utilization. Furthermore, running these workflow implementations in different environments requires significant adaptation efforts, which can hinder the reproducibility of the underlying science. High-level scientific workflow management systems (WMS) can be used to automate and simplify complex task structures by providing tooling for the composition and execution of workflows – even across distributed and heterogeneous computing environments. The WMS approach allows users to focus on the underlying high-level workflow and avoid low-level pitfalls that would lead to non-optimal resource usage while still allowing the workflow to remain portable between different computing environments. As a case study, we apply the UNICORE workflow management system to enable the coupling of a glacier flow model and calving model which contain many tasks and dependencies, ranging from pre-processing and data management to repetitive executions in heterogeneous high-performance computing (HPC) resource environments. Using the UNICORE workflow management system, the composition, management, and execution of the glacier modelling workflow becomes easier with respect to usage, monitoring, maintenance, reusability, portability, and reproducibility in different environments and by different user groups. Last but not least, the workflow helps to speed the runs up by reducing model coupling I/O overhead and it optimizes CPU utilization by avoiding idle CPU cores and running the models in a distributed way on the HPC cluster that best fits the characteristics of each model.


2021 ◽  
Vol 5 (EICS) ◽  
pp. 1-29
Author(s):  
Debasish Chakroborti ◽  
Banani Roy ◽  
Sristy Sumana Nath

To process a large amount of data sequentially and systematically, proper management of workflow components (i.e., modules, data, configurations, associations among ports and links) in a Scientific Workflow Management System (SWfMS) is inevitable. Managing data with provenance in a SWfMS to support reusability of workflows, modules, and data is not a simple task. Handling such components is even more burdensome for frequently assembled and executed complex workflows for investigating large datasets with different technologies (i.e., various learning algorithms or models). However, a great many studies propose various techniques and technologies for managing and recommending services in a SWfMS, but only a very few studies consider the management of data in a SWfMS for efficient storing and facilitating workflow executions. Furthermore, there is no study to inquire about the effectiveness and efficiency of such data management in a SWfMS from a user perspective. In this paper, we present and evaluate a GUI version of such a novel approach of intermediate data management with two use cases (Plant Phenotyping and Bioinformatics). The technique we call GUI-RISPTS (Recommending Intermediate States from Pipelines Considering Tool-States) can facilitate executions of workflows with processed data (i.e., intermediate outcomes of modules in a workflow) and can thus reduce the computational time of some modules in a SWfMS. We integrated GUI-RISPTS with an existing workflow management system called SciWorCS. In SciWorCS, we present an interface that users use for selecting the recommendation of intermediate states (i.e., modules' outcomes). We investigated GUI-RISPTS's effectiveness from users' perspectives along with measuring its overhead in terms of storage and efficiency in workflow execution.


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