Data-centric and data-aware frameworks for fundamentally efficient data handling in modern computing systems

There is an explosive growth in the size of the input and/or intermediate data used and generated by modern and emerging applications. Unfortunately, modern computing systems are not capable of handling large amounts of data efficiently. Major concepts and components (e.g., the virtual memory system) and predominant execution models (e.g., the processor-centric execution model) used in almost all computing systems are designed without having modern applications' overwhelming data demand in mind. As a result, accessing, moving, and processing large amounts of data faces important challenges in today's systems, making data a first-class concern and a prime performance and energy bottleneck in such systems. This thesis studies the root cause of inefficiency in modern computing systems when handling modern applications' data demand, and aims to fundamentally address such inefficiencies, with a focus on two directions. First, we design a new framework that aids the widespread adoption of processing-using-DRAM, a data-centric computation paradigm that improves the overall performance and efficiency of the system when computing large amounts of data by minimizing the cost of data movement and enabling computation where the data resides. To this end, we introduce SIMDRAM, an end-to-end processing-using-DRAM framework that (1) efficiently computes complex operations required by modern data intensive applications, and (2) provides the ability to implement new arbitrary operations as required, all in an in-DRAM massively-parallel Single Instruction Multiple Data (SIMD) substrate that requires minimal changes to the DRAM (Dynamic Random Access Memory) architecture. Second, we design a new, more scalable virtual memory framework that (1) eliminates the inefficiencies of the conventional virtual memory frameworks when handling the high memory demand in modern applications, and (2) is built from the ground up to understand, convey, and exploit data properties, to create opportunities for performance and efficiency improvements. To this end, we introduce the Virtual Block Interface (VBI), a novel virtual memory framework that (1) efficiently handles modern applications' high data demand, (2) conveys properties of different pieces of program data (e.g., data structures) to the hardware and exploits this knowledge for performance and efficiency optimizations, (3) better extracts performance from the wide variety of new system configurations that are designed to process large amounts of data (e.g., hybrid memory systems), and (4) provides all the key features of the conventional virtual memory frameworks, at low overhead.