The key objective of this tutorial is to provide a broad, yet an in-depth survey of the emerging field of co-designing software, hardware, and systems components for accelerating enterprise data management workloads. The overall goal of this tutorial is two-fold. First, we provide a concise system-level characterization of different types of data management technologies, namely, the relational and NoSQL databases and data stream management systems from the perspective of analytical workloads. Using the characterization, we discuss opportunities for accelerating key data management workloads using software and hardware approaches. Second, we dive deeper into the hardware acceleration opportunities using Graphics Processing Units (GPUs) and Field-Programmable Gate Arrays (FPGAs) for the query execution pipeline. Furthermore, we explore other hardware acceleration mechanisms such as single-instruction multiple-data (SIMD) that enables short-vector data parallelism.
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