Kepler Scientific Workflow System
In the Big Data era, often, valuable information gets buried in voluminous amounts of data. Scalability is becoming a prerequisite for applications to be able to efficiently process large-scale datasets. This is where scientific workflows – a software application comprised of computational steps and data tools that scale up to run on high-performance computers, distributed environments, or commercial cloud systems – can make the critical difference. Workflows give you confidence in the accuracy of your results. They are science accelerators because they reduce the time to those results.
The participants will learn how they can turn their scientific computing applications into scalable workflows by analyzing available options, techniques and tools. The focus will be on teaching methodologies to create efficient, scientifically rigorous, scalable workflow applications. Participants will also learn about Kepler, a comprehensive environment of reusable and extensible components to support distributed analysis of large-scale data. In particular, you will learn about:
Distributed platforms and system
Cloud and Big Data
Scalable workflow tools
How to make your science reproducible
Kepler tools to build scalable scientific workflows
The Kepler scientific workflow system is an open-source collaborative platform to serve scientists of all disciplines. Kepler has been successfully used in a wide variety of projects to manage, process, and analyze scientific data. Kepler provides a graphical user interface (GUI) for designing scientific workflows, which are a structured set of tasks linked together to implement a computational solution to a scientific problem. Kepler is a powerful and easy-to-use framework to facilitate High-performance, High-throughput and Big Data applications in scientific workflow systems. Kepler’s modular development approach allows users to build workflows in any domain with minimal effort. Users can leverage the workflow composition and management capabilities of Kepler to deploy algorithms on large scale distributed platforms. Kepler is continuously upgraded to support latest Big Data programing paradigms such as MapReduce and enhance deploying capabilities on modern execution engines like Hadoop, Spark and Stratosphere.
The demo session will familiarize audience with open source Kepler workflow system’s key features that will enable them to kick-start their workflow programing journey. We will then explain how workflow systems can help with rapid development of distributed and parallel applications on top of common computing platforms including NSF XSEDE high performance computing resources, the Amazon cloud and Hadoop. We will illustrate the case using Kepler-based Kepler-based Molecular Dynamics workflow that runs on XSEDE HPC cluster (SDSC Comet). The Molecular Dynamics Computer Aided Drug-Discovery (MDCADD) workflow integrates AMBER software and HPC resources using Kepler scientific workflow system.
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