Application-level scheduling on distributed heterogeneous networks
Supercomputing '96 Proceedings of the 1996 ACM/IEEE conference on Supercomputing
The SDSC storage resource broker
CASCON '98 Proceedings of the 1998 conference of the Centre for Advanced Studies on Collaborative research
MAGNETIC RESONANCE IMAGING (MRI) SIMULATION ON A GRID COMPUTING ARCHITECTURE
CCGRID '03 Proceedings of the 3st International Symposium on Cluster Computing and the Grid
DM2: A Distributed Medical Data Manager for Grids
CCGRID '03 Proceedings of the 3st International Symposium on Cluster Computing and the Grid
Nimrod: a tool for performing parametrised simulations using distributed workstations
HPDC '95 Proceedings of the 4th IEEE International Symposium on High Performance Distributed Computing
File and Object Replication in Data Grids
HPDC '01 Proceedings of the 10th IEEE International Symposium on High Performance Distributed Computing
MPICH-G2: a Grid-enabled implementation of the Message Passing Interface
Journal of Parallel and Distributed Computing - Special issue on computational grids
Grid-Enabled Non-Invasive Blood Glucose Measurement
ICCS '08 Proceedings of the 8th international conference on Computational Science, Part I
Discovering genes-diseases associations from specialized literature using the grid
IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
Simultaneous scheduling of replication and computation for bioinformatic applications on the grid
ISBMDA'05 Proceedings of the 6th International conference on Biological and Medical Data Analysis
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The availability of powerful microprocessors and improvements in the performance of networks has enabled high performance computing on wide-area, distributed systems. Computational grids, by integrating diverse, geographically distributed and essentially heterogeneous resources provide the infrastructure for solving large-scale problems. However, heterogeneity, on the one hand allows for scalability, but on the other hand makes application development and deployment for such an environment extremely difficult.The field of life sciences has seen an explosion in data over the past decade. The data acquired needs to be processed, interpreted and analyzed to be useful. The large resource needs of bioinformatics allied to the large number of data-parallel applications in this field and the availability of a powerful, high performance, computing grid environment lead naturally to opportunities for developing grid-enabled applications. This survey, done as part of the Life Sciences Research Group (a research group belonging to the Global Grid Forum) attempts to collate information regarding grid-enabled applications in this field.