UC Santa Barbara

The advancement of general-purpose architecture has reached a juncture where the continuing investment to improve instruction per cycle (IPC) yields diminishing returns. While domain-specific architecture more efficiently converts silicon resources into throughput, economic viability hinders their wider adoption, except in a few areas. A more feasible way is to extract reusable operations that can be used across multiple domains and then find efficient architecture to support them.

This dissertation focuses on operations involving pairs of keys and values. The application spans a wide range of domains, including database, graph computing, genomics, and sparse computing. The processing of key-value pairs is divided into two categories: ordered and unordered. For the ordered category, we optimized the general merge style operations on a sorted key-value array by creating a set of highly composable primitives. Next, we show that many widely used ordered data structures and algorithms, such as heap and binary search, can be accelerated by rewriting them to use merge operation as a building block. For the unordered ones, we observe that reduce-by-key is a common bottleneck in many domains. We propose the design of the Reduce-By-Key core and introduce a new algorithm to accelerate this operation. We also analytically prove that our method is close to optimal. Lastly, we investigate the decomposition operation on sparse tensors - a special form of key-value pairs. We show how a PE-interactive architecture can be used to significantly increase data reuse.