Parallel simulation of many-core processors: Integration of research and education

Abstract

Providing undergraduate students with an opportunity to experience meaningful academic research has a potential impact on their future career choice. Our approach combines two seemingly contradicting attributes: (i) to make it exciting, the effort targets a grand research objective; and (ii) to make the experience self-assuring and overall positive, the concrete task handed to a student is feasible, given their background and time constraints, while still contributing towards the grand objective. We believe that this can motivate a wider range of undergraduate students, including underrepresented groups of undergraduate engineering students to pursue an engineering career path, academic or otherwise. In this paper, we describe a pilot of an on-going, multiple-year research project, carried out by undergraduate female students incorporating research and education in computer science and engineering (CS&E). Many-core processors are becoming increasingly popular in general-purpose computing. While most researchers agree that this requires introduction of parallelism to mainstream CS&E practice, and hence education, parallel programming difficulties remain obstacles that are yet to be overcome. For concreteness, the research project involves a certain many-core framework, called eXplicit Multi-Threading (XMT). The XMT framework provides a general-purpose many-core architecture for fine-grained parallel programs that scales to a thousand lightweight cores, aiming to improve single task execution time through parallelism. What makes XMT attractive is that it has been supported by significant evidence on ease-of-programming and competitive performance. The XMT platform consists of a proof-of-concept 64-core FPGA and ASIC prototypes and a highly configurable cycle-accurate simulator (XMTSim), capable of modeling a target 1024-core XMT. Our work aims to parallelize XMTSim. (i) The grand objective is to establish that XMT is an effective self-simulating machine; namely, to efficiently simulate the XMTSim code by XMTSim itself. What makes this objective grand, and therefore inspiring, is that one of the elegant features of Turing machines was their ability to provide self-simulations. This feature has been used in support of the thesis that Turing machines are general-purpose. (ii) The combined milestone for the student projects is parallelizing the most computationally time-consuming component of XMTSim, the Interconnection Network (ICN) of XMT. (iii) Each student is being given a part of the job. © 2012 American Society for Engineering Education.