Affiliation:
1. Indian Institute of Technology (IIT) Guwahati, India and SRM University - AP, Andhra Pradesh, India
2. Indian Institute of Technology (IIT) Guwahati, India
3. Indian Institute of Technology (IIT) Kharagpur, India
Abstract
To meet application-specific performance demands, recent embedded platforms often involve the use of intricate micro-architectural designs and very small feature sizes leading to complex chips with multi-million gates. Such ultra-high gate densities often make these chips susceptible to inappropriate surges in core temperatures. Temperature surges above a specific threshold may throttle processor performance, enhance cooling costs, and reduce processor life expectancy. This work proposes a generic temperature management strategy that can be easily employed to adapt existing state-of-the-art task graph schedulers so that schedules generated by them never violate stipulated thermal bounds. The overall temperature-aware task graph scheduling problem has first been formally modeled as a constraint optimization formulation whose solution is shown to be prohibitively expensive in terms of computational overheads. Based on insights obtained through the formal model, a new fast and efficient heuristic algorithm called
TMDS
has been designed. Experimental evaluation over diverse test case scenarios shows that
TMDS
is able to deliver lower schedule lengths compared to the temperature-aware versions of four prominent
makespan
minimizing algorithms, namely,
HEFT
,
PEFT
,
PPTS
, and
PSLS
. Additionally, a case study with an adaptive cruise controller in automotive systems has been included to exhibit the applicability of
TMDS
in real-world settings.
Publisher
Association for Computing Machinery (ACM)
Subject
Electrical and Electronic Engineering,Computer Graphics and Computer-Aided Design,Computer Science Applications
Reference38 articles.
1. List Scheduling Algorithm for Heterogeneous Systems by an Optimistic Cost Table
2. On multiprocessor temperature-aware scheduling problems;Bampis Evripidis;J. Sched.,2013
3. Temperature aware online scheduling for throughput maximisation: The effect of the cooling factor
4. Luiz F. Bittencourt, Rizos Sakellariou, and Edmundo R. M. Madeira. 2010. DAG scheduling using a lookahead variant of the heterogeneous earliest finish time algorithm. In Proceedings of the 18th Euromicro Conference on Parallel, Distributed, and Network-Based Processing (PDP’10). IEEE, 27–34.
5. Comparative Evaluation Of The Robustness Of DAG Scheduling Heuristics