UIO: a uniform I/O system interface for distributed systems
ACM Transactions on Computer Systems (TOCS)
MPI: The Complete Reference
Multiple Bypass: Interposition Agents for Distributed Computing
Cluster Computing
GridLab: Enabling Applications on the Grid
GRID '02 Proceedings of the Third International Workshop on Grid Computing
The Legion Resource Management System
IPPS/SPDP '99/JSSPP '99 Proceedings of the Job Scheduling Strategies for Parallel Processing
Bypass: A Tool for Building Split Execution Systems
HPDC '00 Proceedings of the 9th IEEE International Symposium on High Performance Distributed Computing
Wrapping Legacy Codes for Grid-Based Applications
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
The Grid 2: Blueprint for a New Computing Infrastructure
The Grid 2: Blueprint for a New Computing Infrastructure
The GrADS Project: Software Support for High-Level Grid Application Development
International Journal of High Performance Computing Applications
GridRod: a dynamic runtime scheduler for grid workflows
Proceedings of the 21st annual international conference on Supercomputing
Practical experiences on the gridification of financial applications
Proceedings of the fourth workshop on High performance computational finance
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The GriddLeS (Grid Enabling Legacy Software) middleware is a novel software layer that allows previously separate legacy applications to be coupled in a software workflow over the grid without any changes to the application source code. We have previously tested GriddLeS on a number of applications, including a small atmospheric sciences workflow in which weather and climate models are coupled. In this paper we describe a number of enhancements to the previous implementation [1] [2] that improve its performance significantly. Specifically, the new implementation improves the performance when small blocks are written over high latency networks. The paper also describes a much larger atmospheric sciences workflow than previously reported, that couples multiple global climate models, regional weather models and pollution models in one virtual application.