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This bachelor's thesis explores data locality in heterogeneous memory systems, characterized by advancements in main memory technologies such as Non-Volatile RAM (NVRAM) and High Bandwidth Memory (HBM). Systems equipped with more than one type of main memory or employing a Non-Uniform Memory Architecture (NUMA) necessitate strategic decisions regarding data placement to take advantage of the properties of the different storage tiers. In response to this challenge, Intel has introduced the Data Streaming Accelerator (DSA), which offloads data operations, offering a potential avenue for enhancing efficiency in data-intensive applications. The primary objective of this thesis is to provide a comprehensive analysis and characterization of the architecture and performance of the DSA, along with its application to a domain-specific prefetching methodology aimed at accelerating database queries within heterogeneous memory systems. We contribute a versatile library, capable of performing caching, data replication and prefetching asynchronously, accelerated by the \gls{dsa}.

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