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Benchmarking Multi-Core Performance Using NU-MineBench Suite Modifications

This research utilized the NU-MineBench benchmark suite consisting of twelve data mining applications programmed in C with OpenMP for parallelism. The aim was to modify these applications for compatibility with the GCC compiler, successfully migrating the APR and HOP applications. Performance was evaluated on a twelve-core machine, measuring execution time relative to thread count. Results indicate that execution times decrease up to twelve threads but increase beyond that, illustrating expected multi-core behavior. Future work will explore performance on machines with hundreds of cores.

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Benchmarking Multi-Core Performance Using NU-MineBench Suite Modifications

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  1. Utilization of parallel benchmarks to test performance of multi-core machinesStudent Researcher: ElakianKanakarajMentor: Dr. Donald Yeung Summary: I used the NU-MineBench benchmark suite, which contains twelve different data mining applications. All applications were coded in the C programming language (using an Open-MP library for parallelizing the code), but were complied with the iccintel compiler. I attempted to modify these applications, so that they could be complied using the generic gcc compiler. However, due to many incompatibility issues, I was only able to successfully migrate the APR (apriori) and HOP applications to the gcc compiler implementation. For these two applications, I tested the execution time relative to the number of threads on a machine with twelve cores (results can be seen below). Ultimately, the goal would be to use these benchmarks with machines with hundreds to thousands of cores. Results: The data collected demonstrates an expected behavior. The execution time decreases up to around twelve threads, but subsequently increases afterwards.

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