Application mapping for chip multiprocessors

• Published in: 2008 45th ACM/IEEE Design Automation Conference pp. 620 - 625
• Main Authors: Chen, Guangyu, Li, Feihui, Son, S. W., Kandemir, M.
• Format: Conference Proceeding
• Language: English
• Published: New York, NY, USA ACM 08.06.2008; IEEE
• Series: ACM Conferences
• Subjects: Application Mapping; Application software; Chip Multiprocessing; Compilers; Computer languages; Energy consumption; Energy efficiency; Network-on-a-chip; NoC (Network on Chip); Performance analysis; Power Optimization; Processor scheduling; Routing; Software and its engineering > Software notations and tools > General programming languages; Testing; Yarn; NoC (Network on Chip); application mapping; compilers; chip multiprocessing; power optimization
• ISBN: 1605581151; 9781605581156
• ISSN: 0738-100X
• DOI: 10.1145/1391469.1391628

The problem attacked in this paper is one of automatically mapping an application onto a Network-on-Chip (NoC) based chip multiprocessor (CMP) architecture in a locality-aware fashion. The proposed compiler approach has four major steps: task scheduling, processor mapping, data mapping, and packet routing. In the first step, the application code is parallelized and the resulting parallel threads are assigned to virtual processors. The second step implements a virtual processor-to-physical processor mapping. The goal of this mapping is to ensure that the threads that are expected to communicate frequently with each other are assigned to neighboring processors as much as possible. In the third step, data elements are mapped to memories attached to CMP nodes. The main objective of this mapping is to place a given data item into a node which is close to the nodes that access it. The last step of our approach determines the paths (between memories and processors) for data to travel in an energy efficient manner. In this paper, we describe the compiler algorithms we implemented in detail and present an experimental evaluation of the framework. In our evaluation, we test our entire framework as well as the impact of omitting some of its steps. This experimental analysis clearly shows that the proposed framework reduces energy consumption of our applications significantly (27.41% on average over a pure performance oriented application mapping strategy) as a result of improved locality of data accesses.