Efficient Mapping of Applications for Future Chip-Multiprocessors in Dark Silicon Era

Author:

Hoveida Mohaddeseh1,Aghaaliakbari Fatemeh1,Bashizade Ramin2,Arjomand Mohammad3,Sarbazi-Azad Hamid4

Affiliation:

1. Sharif University of Technology, Tehran, Iran

2. Duke University, NC, USA

3. Pennsylvania State University, State College, PA

4. Sharif University of Technology, Institute for Research in Fundamental Sciences, Tehran, Iran

Abstract

The failure of Dennard scaling has led to the utilization wall that is the source of dark silicon and limits the percentage of a chip that can actively switch within a given power budget. To address this issue, a structure is needed to guarantee the limited power budget along with providing sufficient flexibility and performance for different applications with various communication requirements. In this article, we present a general-purpose platform for future many-core Chip-Multiprocessors (CMPs) that benefits from the advantages of clustering, Network-on-Chip (NoC) resource sharing among cores, and power gating the unused components of clusters. We also propose two task mapping methods for the proposed platform in which active and dark cores are dispersed appropriately, so that an excess of power budget can be obtained. Our evaluations reveal that the first and second proposed mapping mechanisms respectively reduce the execution time by up to 28.6% and 39.2% and the NoC power consumption by up to 11.1% and 10%, and gain an excess power budget of up to 7.6% and 13.4% over the baseline architecture.

Publisher

Association for Computing Machinery (ACM)

Subject

Electrical and Electronic Engineering,Computer Graphics and Computer-Aided Design,Computer Science Applications

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Machine Learning Enabled Solutions for Design and Optimization Challenges in Networks-on-Chip based Multi/Many-Core Architectures;ACM Journal on Emerging Technologies in Computing Systems;2023-06-30

2. Optimal Sprinting Pattern in Thermal Constrained CMPs;IEEE Transactions on Emerging Topics in Computing;2021-01-01

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