Supercomputing over Cloud using the Quicksort algorithm

Abstract: Context: Cloud Computing has advanced in recent years. It is catching people’s attention as a commodious resource of computational power. Slowly, Cloud is bringing new possibilities for a scientific community to build High Performance Computing platforms. Despite the wide benefits the Cloud offers, the question on everyone’s mind is “Whether the Cloud is a feasible platform for HPC applications”. This thesis evaluates the performance of the Amazon Cloud using a sorting benchmark. Objectives: 1. To investigate all the previous work on HPC that has been ported to the Cloud environment in various fields. Also, the problems and challenges are assessed relevant to HPC associated with the Cloud. 2. A study is done on how to implement parallel Quicksort efficiently to obtain good Speedup. 3. A parallel Quicksort is developed and its performance is measured using ‘Speedup’ by deploying in the Cloud. Methods: Two different research methods were used to carry out the research. They are Systematic Literature Review (SLR) and a Quantitative methodology. Research papers from academic databases namely IEEE Xplore, Inspec, ACM Digital Library and Springerlink were chosen for conducting SLR. Results: From the systematic review undertaken, 12 HPC applications, 9 problems and 5 challenges in the Cloud were identified. Efficient way to implement the parallel Quicksort on the Cloud has been identified. From the experiment results, a low Speedup is obtained in a Cloud environment. Conclusions: Many HPC applications which were deployed in the Cloud so far were identified along with problems and challenges. Message Passing interface (MPI) is chosen as the efficient method to develop and implement the parallel Quicksort in the Cloud. From the experiment results, we believe that the Cloud is not a suitable platform for HPC applications.