André Giesler

The UNICORE Rich Client: Facilitating the Automated Execution of Scientific Workflows

Today, many scientific disciplines heavily rely on computer systems for in-silico experimentation or data management and analysis. The employed computer hard- and software is heterogeneous and complies to different standards, interfaces and protocols for interoperation. Grid middleware systems like UNICORE 6 try to hide some of the complexity of the underlying systems by offering high-level, uniform interfaces for executing computational jobs or storing, moving, and searching through data. Via UNICORE 6 computer resources can be accessed securely with different software clients, e.g. the UNICORE Command line Client (UCC) or the graphical UNICORE Rich Client (URC) which is based on Eclipse. In this paper, we describe the design and features of the URC, and highlight its role as a flexible and extensible Grid client framework using the QSAR field as an example.

Experiences with Using UNICORE in Production Grid Infrastructures DEISA and D-Grid

The early stages of Grid Computing focussed on developing concepts and prototype software. Grid middleware is now at a phase of some maturity and today the focus has shifted to providing production ready Grid environments for scientists. DEISA and D-Grid aim for this production scope. DEISA is the consortium of leading national European supercomputing centres that currently deploys and operates a persistent, production quality, and distributed supercomputing environment in Europe. D-Grid is the German Grid initiative that builds up and operates a sustainable Grid infrastructure and establishes methods of e-science in the German scientific community. UNICORE is used in DEISA and D-Grid as the interface to heterogeneous production resources, and hides the complexity of resources from users. UNICORE is one of the leading Grid middleware systems used in production Grid infrastructures worldwide. This paper describes the experiences with UNICORE as Grid middleware in e-infrastructures comprising production systems in production use.

UniProv: A Flexible Provenance Tracking System for UNICORE

In this paper we present a flexible provenance management system called UniProv. UniProv is an ongoing development project providing provenance tracking in scientific workflows and data management particularly in the field of neuroscience, thus allowing users to validate and reproduce tasks and results of their experiments.

UniProv - Provenance Management for UNICORE Workflows in HPC Environments.

The goal of comprehensive provenance tracking in the scientific environment should be the inclusion of the entire life cycle of data management. Thus, the data collection process begins with the registration of lab-generated or sensor-generated data, continues to organize and manage data in the storage repositories, processing analysis and simulation data on clusters and HPC systems, and finally referencing and verifying computational results in scientific publications. In the associated provenance tracking life cycle, UniProv initially concentrates on the processing and simulation of data in scientific workflows used in particular on supercomputers in the HPC environment. In this context, UniProv aims to create the core of a provenance management framework that can be extended in order to integrate different sources of the scientific provenance cycle. Here UniProv should facilitate the creation, the standardized formalization, the storage and the retrieval of Provenance Information.

Advancing data management and analysis in different scientific disciplines

Over the past several years, rapid growth of data has affected many fields of science. This has often resulted in the need for overhauling or exchanging the tools and approaches in the disciplines’ data life cycles. However, this allows the application of new data analysis methods and facilitates improved data sharing. The project Large-Scale Data Management and Analysis (LSDMA) of the German Helmholtz Association has been addressing both specific and generic requirements in its data life cycle successfully since 2012. Its data scientists work together with researchers from the fields such as climatology, energy and neuroscience to improve the community-specific data life cycles, in several cases even all stages of the data life cycle, i.e. from data acquisition to data archival. LSDMA scientists also study methods and tools that are of importance to many communities, e.g. data repositories and authentication and authorization infrastructure.