Mohammad Mahdi Jaghoori

A data-centric neuroscience gateway: design, implementation, and experiences

Science gateways provide UIs and high‐level services to access and manage applications and data collections on distributed resources. They facilitate users to perform data analysis on distributed computing infrastructures without getting involved into the technical details. The e‐BioInfra Gateway is a science gateway for biomedical data analysis on a national grid infrastructure, which has been successfully adopted for neuroscience research. This paper describes the motivation, requirements, and design of a new generation of e‐BioInfra Gateway, which is based on the grid and cloud user support environment (also known as WS‐PGRADE/gUSE framework) and supports heterogeneous infrastructures. The new gateway has been designed to have additional data and meta‐data management facilities to access and manage (biomedical) data servers, and to provide data‐centric user interaction. We have implemented and deployed the new gateway for the computational neuroscience research community of the Academic Medical Center of the University of Amsterdam. This paper presents the system architecture of the new gateway, highlights the improvements that have been achieved, discusses the choices that we have made, and reflects on those based on initial user feedback. Copyright

1001 Ways to run AutoDock Vina for virtual screening

Large-scale computing technologies have enabled high-throughput virtual screening involving thousands to millions of drug candidates. It is not trivial, however, for biochemical scientists to evaluate the technical alternatives and their implications for running such large experiments. Besides experience with the molecular docking tool itself, the scientist needs to learn how to run it on high-performance computing (HPC) infrastructures, and understand the impact of the choices made. Here, we review such considerations for a specific tool, AutoDock Vina, and use experimental data to illustrate the following points: (1) an additional level of parallelization increases virtual screening throughput on a multi-core machine; (2) capturing of the random seed is not enough (though necessary) for reproducibility on heterogeneous distributed computing systems; (3) the overall time spent on the screening of a ligand library can be improved by analysis of factors affecting execution time per ligand, including number of active torsions, heavy atoms and exhaustiveness. We also illustrate differences among four common HPC infrastructures: grid, Hadoop, small cluster and multi-core (virtual machine on the cloud). Our analysis shows that these platforms are suitable for screening experiments of different sizes. These considerations can guide scientists when choosing the best computing platform and set-up for their future large virtual screening experiments.

A Grid-Enabled Virtual Screening Gateway

In computer-aided drug design, software tools are used to narrow down possible drug candidates, therefore reducing the amount of expensive in vitro research by a process called virtual screening. However, searching for drug candidates among a huge number of alternatives requires extensive computation. In this paper, we describe a science gateway for virtual screening that has been tailored to the specific needs of our local users. By reusing the generic architecture and code of a previously developed science gateway for another scientific discipline, it took us only two months from early requirements analysis to obtain a running gateway. The early empirical results show (1) considerable speed-ups, thanks to usage of grid infrastructure, and, (2) user satisfaction, thanks to the user-centred design of the web interface and automated data management.

Scientific Workflow Management -- For Whom?

Workflow management has been widely adopted by scientific communities as a valuable tool to carry out complex experiments. It allows for the possibility to perform computations for data analysis and simulations, whereas hiding details of the complex infrastructures underneath. There are many workflow management systems that offer a large variety of generic services to coordinate the execution of workflows. Nowadays, there is a trend to extend the functionality of workflow management systems to cover all possible requirements that may arise from a user community. However, there are multiple scenarios for usage of workflow systems, involving various actors that require different services to be supported by these systems. In this paper we reflect about the usage scenarios of scientific workflow management based on the practical experience of heavy users of workflow technology from communities in three scientific domains: Astrophysics, Heliophysics and Biomedicine. We discuss the requirements regarding services and information to be provided by the workflow management system for each usage profile, and illustrate how these requirements are fulfilled by the tools these communities currently adopt. This paper contributes to the understanding of properties of future workflow management systems that are important to increase their adoption in a large variety of usage scenarios.

Understanding workflows for distributed computing: nitty-gritty details

Scientific workflow management is heavily used in our organization. After six years, a large number of workflows are available and regularly used to run biomedical data analysis experiments on distributed infrastructures, mostly on grids. In this paper we present our first efforts to better understand and characterise these workflows. We start with a set of considerations previously proposed in the literature (workflow dimensions and motifs), and revise these to more closely describe what we observe in our workflows. We conclude that workflow characteristics can be categorized at two levels: firstly, the features characterizing the distributed application and how to implement it as a workflow, and secondly, workflow motifs that depend on the features of the selected workflow management system. These characteristics could be useful in the future to understand a larger set of workflows and to identify functional requirements for further development workflow management systems.

A multi-infrastructure gateway for virtual drug screening

In computer‐aided drug design, software tools are used to narrow down possible drug candidates, thereby reducing the amount of expensive in vitro research, by a process called virtual screening. This process includes large computations that require advanced computing infrastructure; however, using rapidly evolving high‐performance computing platforms can be difficult for biochemists. In this paper, we present a science gateway for virtual screening that has been tailored to the specific needs of our local users. The gateway provides user‐friendly access to distributed computing infrastructures for high‐throughput experiments with a few clicks. Its design is based on the generic layer developed for another gateway for neuroimaging data analysis, including data and computation management, as well as support for its operation. To facilitate scalability, the system architecture allows for adding new computing platforms to the back‐end without affecting the front‐end, from which the user can dynamically choose the preferred infrastructure. This paper describes the user‐centered design process, the system architecture and a performance assessment using gLite grid, Hadoop, and a local cluster. The empirical results show considerable speed‐ups and ease of use, as well as user satisfaction. Copyright

WS-PGRADE/gUSE-Based Science Gateways in Teaching

Various WS-PGRADE/gUSE science gateways have been extensively used in educational contexts, supporting courses offered by different European universities and organizations. This chapter presents some examples of how WS-PGRADE/gUSE generic and customized gateways have been used in such courses. These examples include practical cases from a variety of scientific fields and educational styles. For each case, the educational context and the course organization are presented, with emphasis on how the respective portal has been adopted for the practical exercises. A summary of experiences are also reported, including advantages and difficulties faced for using these gateways in teaching.

Computational Neuroscience Gateway: A Science Gateway Based on the WS-PGRADE/gUSE

Computational neuroscientists face challenges to manage ever-increasing large volume of data and to process them with applications that require great computational power. The Brain Imaging Centre of the Academic Medical Centre of the University of Amsterdam is a community of neuroscientists who are involved in various computational neuroscience research studies. They face various challenges to process and manage a growing amount of neuroimaging data. The goal of the computational neuroscience gateway is to facilitate large-scale data processing on distributed infrastructures and to enhance data management and collaboration for scientific research. The gateway is based on the WS-PGRADE/gUSE generic science gateway framework as platform for distributed computing, and it is connected to a data server based on the eXtensible Neuroimaging Archive Toolkit (XNAT). This chapter presents the design and architecture of the gateway with focus on the utilization of the WS-PGRADE/gUSE framework, and the lessons learned during its implementation and operation.

Processing Manager for Science Gateways

In this paper, we define the concept of Processing in the context of science gateways (SGs), and the necessary management layer that is needed to handle it. A Processing captures the execution/run of a scientific Application, the Data consumed and produced during that run, and the User that performs it. The properties that define Application, Data and User are different across various layers, from the scientific domain to the infrastructure. We describe a software layer, the Processing Manager (PM), that manages the translation of a Processing from the domain level to the infrastructure level and back. The PM offers a homogeneous abstraction layer over heterogeneous execution platforms and data services for various infrastructures, thus simplifying the development, maintenance and operation of SGs. Finally, the PM enables external high-level control and monitoring of the execution process and naturally captures the provenance of each processing.

User-oriented partial result evaluation in workflow-based science gateways

Scientific workflow management systems provide a useful layer for defining and executing applications supported by science gateways. In various optimization or simulation applications that need to run for a long time, the users may be satisfied with an incomplete execution. The system should, therefore, allow users to evaluate partial results of the workflow execution. This entails performing a consolidation step, that would normally run only at the end of the workflow. In this paper, we present two new workflow patterns that formally define how the consolidation step should be executed (on partial inputs) whenever the user proactively requests evaluation of the partial results. This changes the traditional workflow behavior, in which every step runs once, when all its data dependencies are satisfied. We evaluate implementing these patterns in various workflow management systems and finally present a DIRAC-based implementation of this feature for the use case of a molecular docking gateway.