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Grid-Enabling Resource-Intensive Applications


Conclusion

Most of the architectures and communication strategies we have discussed here have similarities and overlaps because they have all basically evolved from the same concepts, or from each other. This makes the comparisons a bit blurred. Also, there are many different programming languages available for use, and any one of them could probably have been used to develop this software. We chose Java mainly because we felt it had the most to offer as far as applicable library routines, and would be the least restrictive as far as future portability. Also we felt development time would be shorter because of Java's lack of pointer issues. Our other decisions were of course heavily influenced by our choice to use Java.

From our research we decided that RMI was the most advanced technology to use for the communications between client and server. From what we read we knew that JNI would be difficult to use but that a JNI wrapper would be needed for the function calls to the CodeMatch DLL. As it turned out, we ran across SWIG (www.ddj.com/184410484),, which can automatically generate JNI wrappers for programs written in a multitude of languages. While SWIG had a learning curve of its own, the automation of wrapper generation probably saved us a couple of weeks on this project.

The architecture we chose is a distributed objects model. The workers all remotely call methods on the master and vice-versa. The program uses the "pull" model with the workers requesting new jobs as they complete jobs. Each worker sends back the results of its work and then the master sorts and formats all results into a database file from which various types of end-user reports can be generated.

The resulting performance from this project has been excellent with the run times being cut almost by the number of worker machines. While small jobs (fewer than 40 files or so) are still quicker with the standalone CodeMatch due to the extra overhead of the network initialization, overall we can now run large jobs in a fraction of the time it once took.


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