Paper Info
Title
Task mapping in heterogeneous embedded systems for fast completion time
Abstract
Graphics processing units are being widely used in embedded systems as they can achieve high performance and energy efficiency. In such systems, the problem of computation and data mapping for multiple applications while minimizing the completion time is quite challenging due to a large size of the policy space, including heterogeneous application characteristics, complex application structure, data communication costs, and data partitioning. To achieve fast competition time, a fine-grain mapping framework that explores a set of critical factors is needed for heterogeneous embedded systems. In this paper, we consider this mapping problem by presenting a theoretical framework that yields an optimal integer programming solution. Moreover, based upon several interesting measurements-based case studies, we design three practical mapping algorithms with low time complexity, each of which explores a specific set of factors that may affect the completion time performance. We evaluated the proposed algorithms by implementing them on a real heterogeneous system and using a large set of popular benchmarks for evaluation. Experimental results demonstrate that our proposed algorithms can achieve up to 30% faster completion time compared to the state-of-the-art mapping techniques, and can perform consistently well across different workloads.
Year
DOI
Venue
2014
10.1145/2656045.2656074
EMSOFT
Keywords
Field
DocType
real-time systems and embedded systems,algorithms,design,power aware computing,task mapping,policy space,gpu,experimentation,data mapping,time complexity,completion time performance,practical mapping algorithms,data partitioning,task analysis,heterogeneous systems,data communication costs,graphics processing units,integer programming,computational complexity,completion time minimization,heterogeneous application characteristics,heterogeneousus scheduling,optimal integer programming solution,complex application structure,heterogeneous embedded systems,performance evaluation,embedded systems,energy efficiency,performance,heterogeneous scheduling,fine-grain mapping framework
Graphics,Critical factors,Efficient energy use,Computer science,Data mapping,Task mapping,Real-time computing,Integer programming,Time complexity,Embedded system,Computation,Distributed computing
Conference
Citations 
PageRank 
References 
7
0.47
22
Authors
2
Name
Order
Citations
PageRank
Husheng Zhou1564.95
Cong Liu278056.17