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This bachelor's thesis explores the dynamic landscape of heterogeneous memory systems, characterized by advancements in main memory technologies such as Non-Volatile RAM (NVRAM), High Bandwidth Memory (HBM), and Remote Memory. These systems necessitate strategic decisions regarding data placement to optimize performance, requiring the movement of data across different storage tiers. Consequently, the responsibility for maintaining optimal data placement falls upon the CPU, resulting in a reduction of available cycles for computational tasks. 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.

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