Data and Instruction Uniformity in Minimal Multi-threading

Abstract

Simultaneous Multi-Threading (SMT) is a hardware model in which different threads share the same instruction fetching unit. This model is a compromise between high parallelism and low hardware cost. Minimal Multi-Threading (MMT) is a technique recently proposed to share instructions and execution between threads in a SMT machine. In this paper we propose new ways to explore redundancies in the MMT execution model. First, we propose and evaluate a new thread reconvergence heuristics that handles function calls better than previous approaches. Second, we demonstrate the existence of substantial regularity in inter-thread memory access patterns. We validate our results on the four data-parallel applications present in the PARSEC benchmark suite. The new thread reconvergence heuristics is, on the average, 82% more efficient than MMT's original reconvergence method. Furthermore, about 69% to 87% of all the memory addresses are either the same for all the threads, or are affine expressions of the thread identifier. This observation motivates the design of newly proposed hardware that benefits from regularity in inter-thread memory accesses.