This chapter explores the crucial alternative to traditional data processing methods, focusing on in-memory data processing. It discusses storing large volumes of data in DRAM for efficient and rapid data access, while using disk and SSD storage mainly for backup and archival purposes. The chapter sheds light on the benefits and significance of this approach, emphasizing its role in enabling efficient computing tasks. It also examines the implications of this shift for disk utilization, highlighting the transition towards using disk and SSD storage as secondary mediums, rather than primary data sources.

This chapter delves into the management of structured data using GPUs. It demonstrates the construction of a GPU-based SQL database engine, encompassing both hash-based and sorting-based relational operator algorithms. The chapter explores how complex SQL concepts like subqueries can be efficiently interacted with GPUs for optimal performance, offering insights into the advancements and potential of GPU computing in structured data management.

This opening chapter provides a historical perspective on the evolution of computing, tracing its journey from early computational methods to the emergence of networking and the advent of data-centric computing. The chapter sets out to inspire readers to develop a holistic understanding of the intricate interactions among hardware, software, and networking. It introduces the principle of hardware and software codesign as a critical approach in constructing efficient data management systems. The goal is to achieve high throughput and low latency in modern data processing, setting the stage for the detailed exploration that follows in subsequent chapters.

The concluding chapter consolidates the key learnings and projects future trends and emerging technologies in the dynamic field of data management and computing. It explores how the convergence of advanced hardware, sophisticated algorithms, and AI-driven solutions is shaping the next frontier of data management and computing. Emphasizing practical implications and future possibilities, this final chapter aims to equip readers with a comprehensive understanding and vision of how these integrated technologies will continue to transform the landscape of computing and data management.

This chapter delves into the transformative world of ray tracing, a technology reshaping computational graphics and data processing. It bridges the gap between advanced graphical rendering and general computational tasks, exploring how ray tracing hardware, originally designed for stunning visual effects, is now being harnessed for diverse applications beyond graphics. The chapter employs Nvidia GPU RT Cores and the OptiX programming framework as conduits to explain ray tracing’s fundamental concepts and practical implementations.

This chapter offers a comprehensive examination of main memory, considering both its architectural aspects and its critical role in systems software. The discussion includes the utilization of physical memory addresses as a linkage mechanism, connecting programs in virtual space to their corresponding execution spaces in the cache and main memory. The chapter also presents advancements in CPU and memory products, elucidating their relevance to memory management. Additionally, it introduces the concept of the OS buffer cache and the development of a key–value store at the user level, highlighting their significance in the broader context of data management systems.

Providing a comprehensive introduction to GPU programming and its application in data management, this chapter uses sorting algorithms as a case study. It explores how parallel programming and architecture-oriented performance tuning are integral to unlocking the full potential of GPUs as powerful computing devices. The chapter takes readers through the transformation process of a sequential bubble sorting algorithm into GPU-friendly bitonic sorting and odd–even merging sorting algorithms, illustrating the capabilities and advantages of GPU computing in data management.

Focusing on the intricate world of spatial data management, this chapter offers an in-depth analysis of how spatial data management tasks, specifically in the context of pathology imaging applications, are approached and optimized on traditional CPU-based computing platforms versus GPU-accelerated platforms. Employing a case-study methodology, the chapter not only delves into the specifics of these applications but also extrapolates broader methodologies and strategies for leveraging advanced hardware to enhance application performance.

Addressing the challenges associated with data movement within the memory hierarchy, this chapter explores solutions from both hardware and systems software perspectives. It places special emphasis on buffer management techniques aimed at optimizing data movement and reducing access latency. The chapter also delves into the significance of nonvolatile memory (NVM), particularly flash memory devices, and their role in mitigating access latency within the memory hierarchy. Readers gain insights into strategies employed to minimize data movement, enhancing overall memory performance, a critical aspect of efficient data management.