Riccardo Murri

TRAL: tandem repeat annotation library

MOTIVATION Currently, more than 40 sequence tandem repeat detectors are published, providing heterogeneous, partly complementary, partly conflicting results. RESULTS We present TRAL, a tandem repeat annotation library that allows running and parsing of various detection outputs, clustering of redundant or overlapping annotations, several statistical frameworks for filtering false positive annotations, and importantly a tandem repeat annotation and refinement module based on circular profile hidden Markov models (cpHMMs). Using TRAL, we evaluated the performance of a multi-step tandem repeat annotation workflow on 547 085 sequences in UniProtKB/Swiss-Prot. The researcher can use these results to predict run-times for specific datasets, and to choose annotation complexity accordingly. AVAILABILITY AND IMPLEMENTATION TRAL is an open-source Python 3 library and is available, together with documentation and tutorials via http://www.vital-it.ch/software/tral. CONTACT elke.schaper@isb-sib.ch.

AppPot: bridging the Grid and Cloud worlds

This paper presents AppPot, a system for creating Linux software appliances. AppPot appliances can be run as a regular batch or grid job and executed in user space, and require no virtualization support in the infrastructure. The main design goal of AppPot is to bring the benefits of a virtualization-based IaaS cloud to existing batch-oriented computing infrastructures. In particular, AppPot addresses the application deployment and configuration on large heterogeneous computing infrastructures: users are able to prepare their own customized virtual appliance to provide a safe execution environment for their applications. These appliances can then be executed on virtually any computing infrastructure, being it a private or public cloud, as well as any batch-queueing compute cluster. We give an overview of AppPot and its features, the technology that makes it possible, and briefly report on experiences running it in production use within the Swiss national grid infrastructure SMSCG.

GC3Pie: A Python framework for high-throughput computing

This paper present GC3Pie [7], a python library to ease the development of scalable and robust High Throughput data analysis tools. Most of the current distributed computing middlewares as well as most of the in-house grown scripts fall short in reaching the scaling and reliability factors required by the ever growing demand of large data analysis. GC3Pie provides mechanisms to automitise the execution and the monitoring of large collection of applications while, at the same time, provides simple data structures and interfaces to steer the behaviour of the underlying system in an application-centric perspective. The goal of GC3Pie is to embody the common execution and monitorig processing part of large data analysys while moving most decision making logic to the application level; like, for example, the reaction of certain types of failures, the validation of the application execution or the brokering of the computing resources driven by application fidelity metrics. This allows to write application specific tools that take full control of the underlying computing and data infrastructure, as opposite of current middleware stacks that are trying to embody the full control of the execution logic thus reducing the flexibility of the entire system as they prevent applications to define their own expected behaviour of the system.

Milu, the three middleware user interface

Many Grid infrastructures are nowadays serving the scientific communities. Those infrastructures are routinely accessed through some client software generally called: User Interface (UI). Installing and configuring such Grid clients is a challenging task for non expert scientific users even today. Miramare Interoperable Lite User Interface (MILU), is a software which targets mostly this kind of users by providing them a Plug&Play command line interface which contains multiple clients for accessing the gLite, ARC and GT4 middlewares. The package has been developed and evolved significantly during time. Through this evolution process we have always considered the incoming users’ requests as natural requisites for the future development. This strategy provides the evident benefit of having a software which better accomplish its purpose, which, in MILU, is to allow a concurrent usage of different Grid infrastructures and create the foundation for more complex scientific gateways. This paper provides a complete overview of MILU and reports on its usage experiences gathered in the EU-IndiaGrid and e-NMR communities, and as a production UI in our local Grid environment.

The Swiss ATLAS Grid

In this paper the technical solutions, the usage and the future development of the Swiss ATLAS Grid are presented. In 2009 the Swiss ATLAS Grid consists of four clusters with about 2000 shared computing cores and about 250 TB of disk space. It is based on middlewares provided by the NorduGrid Collaboration and the EGEE project. It supports multiple virtual organisations and uses additional middleware, developed by the ATLAS collaboration, for data management. The Swiss ATLAS grid is interconnected with both NorduGrid and the Worldwide LHC Grid. This infrastructure primarly serves Swiss research institutions working within the ATLAS experiment at LHC, but is open for about two thousand users on lower priority. The last three years about 80 000 wall clock time days have been processed by ATLAS jobs on the Swiss ATLAS Grid.