A Grid framework to enable parallel and concurrent TMA image analyses

The Grid represents a great opportunity for the scientific community to solve computing intensive problems and share data. To actually allow its use, it is necessary to provide specific tools and enable a simplified exploitation of Grid resources. We address these topics in bioinformatics community to process images obtained through Tissue MicroArray technique. We developed Grid Framework for Tissue Microarray Analysis, shortly GF4TMA, that allows the selection and the analysis of TMA images on the Grid. In particular, users can analyse several images concurrently, and analyses are performed in parallel using Parallel IMAGE processing GEnoa Library, shortly PIMA(GE)² Lib. Therefore GF4TMA enables two levels of parallelism, the scheduling and the parallelism of the analyses are hidden to the users. A particular emphasis is posed on the two levels of parallelism provided by GF4TMA, and its effectiveness is discussed simulating different scenarios in Grid.

[1]  Andrea Clematis,et al.  An Object Interface for Interoperability of Image Processing Parallel Library in a Distributed Environment , 2005, ICIAP.

[2]  Andrea Clematis,et al.  Parallel I/O Aspects in PIMA(GE) , 2007, PARCO.

[3]  Harry M. Sneed,et al.  Integrating legacy software into a service oriented architecture , 2006, Conference on Software Maintenance and Reengineering (CSMR'06).

[4]  Sathish S. Vadhiyar,et al.  Numerical Libraries and the Grid , 2001, Int. J. High Perform. Comput. Appl..

[5]  William Gropp,et al.  PETSc 2.0 users manual , 2000 .

[6]  Dennis Koelma,et al.  User transparency: a fully sequential programming model for efficient data parallel image processing , 2004, Concurr. Pract. Exp..

[7]  Jack Dongarra,et al.  ScaLAPACK user's guide , 1997 .

[8]  Ian Foster,et al.  The Grid 2 - Blueprint for a New Computing Infrastructure, Second Edition , 1998, The Grid 2, 2nd Edition.

[9]  Ian J. Taylor,et al.  Distributed computing with Triana on the Grid , 2005, Concurr. Pract. Exp..

[10]  Gábor Terstyánszky,et al.  GEMLCA: Running Legacy Code Applications as Grid Services , 2005, Journal of Grid Computing.

[11]  J. Kononen,et al.  Tissue microarrays for high-throughput molecular profiling of tumor specimens , 1998, Nature Medicine.

[12]  Spyro Mousses,et al.  Clinical validation of candidate genes associated with prostate cancer progression in the CWR22 model system using tissue microarrays. , 2002, Cancer research.

[13]  Péter Kacsuk,et al.  Multi-Grid, Multi-User Workflows in the P-GRADE Grid Portal , 2005, Journal of Grid Computing.

[14]  Yan Huang,et al.  Wrapping legacy codes for Grid-based applications , 2003, Proceedings International Parallel and Distributed Processing Symposium.

[15]  Andrea Schenone,et al.  GEMMA - A Grid environment for microarray management and analysis in bone marrow stem cells experiments , 2007, Future Gener. Comput. Syst..

[16]  Andrea Clematis,et al.  Tissue MicroArray: a Distributed Grid Approach for Image Analysis , 2007, HealthGrid.

[17]  Cristina Boeres,et al.  EasyGrid: towards a framework for the automatic Grid enabling of legacy MPI applications , 2004, Concurr. Pract. Exp..

[18]  Ivan Merelli,et al.  Ontology-based, Tissue MicroArray oriented, image centered tissue bank , 2008, BMC Bioinformatics.

[19]  Joseph N. Wilson,et al.  Handbook of computer vision algorithms in image algebra , 1996 .

[20]  Andrea Clematis,et al.  Resource Selection and Application Execution in a Grid: A Migration Experience from GT2 to GT4 , 2006, OTM Conferences.