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The Galois system provides a solution to the hard problem of parallelizing irregular algorithms using amorphous data-parallelism. The present system works on the shared-memory programming model. The programming model has limitations on the memory and processing power available to the application. A scalable distributed parallelization tool would give the application access to a very large amount of memory and processing power by interconnecting computers through a network. This thesis presents the design for a distributed execution programming model for the Galois system. This distributed Galois system is capable of executing irregular graph based algorithms on a distributed environment. The API and programming model of the new distributed system has been designed to mirror that of the existing shared-memory Galois. This was done to enable existing applications on shared memory applications to run on distributed Galois with minimal porting effort. Finally, two existing test cases have been implemented on distributed Galois and shown to scale with increasing number of hosts and threads. / text

Existing frequent-sequence mining algorithms perform multiple scans of a database, or a structure that captures the database. In this M.Sc. thesis, I propose a frequent-sequence mining algorithm that mines each database row as it reads it, so that it can potentially complete mining in the time it takes to read the database once. I achieve this by having my algorithm enumerate all sub-sequences from each row as it reads it. Since sub-sequence enumeration is a time-consuming process, I create a method to distribute the work over multiple computers, processors, and thread units, while balancing the load between all resources, and limiting the amount of communication so that my algorithm scales well in regards to the number of computers used. Experimental results show that my algorithm is effective, and can potentially complete the mining process in near the time it takes to perform one scan of the input database.

Existing frequent-sequence mining algorithms perform multiple scans of a database, or a structure that captures the database. In this M.Sc. thesis, I propose a frequent-sequence mining algorithm that mines each database row as it reads it, so that it can potentially complete mining in the time it takes to read the database once. I achieve this by having my algorithm enumerate all sub-sequences from each row as it reads it. Since sub-sequence enumeration is a time-consuming process, I create a method to distribute the work over multiple computers, processors, and thread units, while balancing the load between all resources, and limiting the amount of communication so that my algorithm scales well in regards to the number of computers used. Experimental results show that my algorithm is effective, and can potentially complete the mining process in near the time it takes to perform one scan of the input database.

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