Abstract
The emergence of big data frameworks requires computational and memory resources that can naturally scale to manage massive amounts of diverse data. It is currently unclear whether big data frameworks such as Hadoop, Spark, and MPI will require high bandwidth and large capacity memory to cope with this change. The primary purpose of this study is to answer this question through empirical analysis of different memory configurations available for commodity server and to assess the impact of these configurations on the performance Hadoop and Spark frameworks, and MPI based applications. Our results show that neither DRAM capacity, frequency, nor the number of channels play a critical role on the performance of all studied Hadoop as well as most studied Spark applications. However, our results reveal that iterative tasks (e.g. machine learning) in Spark and MPI are benefiting from a high bandwidth and large capacity memory.
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