Data-Centric and Data-Aware Frameworks for Fundamentally Efficient Data Handling in Modern Computing Systems
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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 SIMDRAM, an end-to-end processing-using-DRAM 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. Second, we introduce the Virtual Block Interface (VBI), a novel 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.
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