Empirical Study of Data Allocation in Heterogeneous Memory

With the rapid development of data-driven technologies, implementing heterogeneous memories is an alternative for processing large-size data tasks or efficient computations while considering economic factors. Many previous studies have addressed the exploration of adopting heterogeneous memories in the field of the algorithm design. One of the vital components of using the heterogeneous memory is creating effective data allocation plans. However, it is challenge to discern the superiority of each method for generating data allocation plans due to various application scenarios and constraints. In this work, we have completed an empirical study focusing recent advanced data allocation mechanisms for heterogeneous memories. We use experimental evaluations to examine a number of representative strategies and the main findings of this work also include analyses and syntheses deriving from our evaluations.

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