Different parallelization methods vary in their system requirements, programming styles, efficiency of exploring parallelism, and the application characteristics they can handle. Different applications can exhibit totally different performance gains depending on the parallelization method used. The paper compares OpenMP, MPI, and Strings (a distributed shared memory) for parallelizing a complicated tribology problem. The problem size and computing infrastructure are changed and their impacts on the parallelization methods are studied. All of the methods studied exhibit good performance improvements. The paper exhibits the benefits that are the result of applying parallelization techniques to applications in this field.
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