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The Complexity of Optimal Job Co-Scheduling on Chip Multiprocessors and Heuristics-Based Solutions
July 2011 (vol. 22 no. 7)
pp. 1192-1205
ASCII Text
x 
 
Yunlian Jiang, Kai Tian, Xipeng Shen, Jinghe Zhang, Jie Chen, Rahul Tripathi, "The Complexity of Optimal Job Co-Scheduling on Chip Multiprocessors and Heuristics-Based Solutions," IEEE Transactions on Parallel and Distributed Systems, vol. 22, no. 7, pp. 1192-1205, July, 2011.
 
 
BibTex
x
 
@article{ 10.1109/TPDS.2010.193,
author = {Yunlian Jiang and Kai Tian and Xipeng Shen and Jinghe Zhang and Jie Chen and Rahul Tripathi},
title = {The Complexity of Optimal Job Co-Scheduling on Chip Multiprocessors and Heuristics-Based Solutions},
journal ={IEEE Transactions on Parallel and Distributed Systems},
volume = {22},
number = {7},
issn = {1045-9219},
year = {2011},
pages = {1192-1205},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPDS.2010.193},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Parallel and Distributed Systems
TI - The Complexity of Optimal Job Co-Scheduling on Chip Multiprocessors and Heuristics-Based Solutions
IS - 7
SN - 1045-9219
SP1192
EP1205
EPD - 1192-1205
A1 - Yunlian Jiang,
A1 - Kai Tian,
A1 - Xipeng Shen,
A1 - Jinghe Zhang,
A1 - Jie Chen,
A1 - Rahul Tripathi,
PY - 2011
KW - Co-scheduling
KW - shared cache
KW - CMP scheduling
KW - cache contention
KW - perfect matching
KW - integer programming.
VL - 22
JA - IEEE Transactions on Parallel and Distributed Systems
ER -
 
Yunlian Jiang, The College of William and Mary, Williamsburg
Kai Tian, The College of William and Mary, Williamsburg
Xipeng Shen, The College of William and Mary, Williamsburg
Jinghe Zhang, University of North Carolina at Chapel Hill, Chapel Hill
Jie Chen, the Thomas Jefferson National Accelerator Facility, VA
Rahul Tripathi, University of South Florida, Tampa
In Chip Multiprocessors (CMPs) architecture, it is common that multiple cores share some on-chip cache. The sharing may cause cache thrashing and contention among co-running jobs. Job co-scheduling is an approach to tackling the problem by assigning jobs to cores appropriately so that the contention and consequent performance degradations are minimized. Job co-scheduling includes two tasks: the estimation of co-run performance, and the determination of suitable co-schedules. Most existing studies in job co-scheduling have concentrated on the first task but relies on simple techniques (e.g., trying different schedules) for the second. This paper presents a systematic exploration to the second task. The paper uncovers the computational complexity of the determination of optimal job co-schedules, proving its NP-completeness. It introduces a set of algorithms, based on graph theory and Integer/Linear Programming, for computing optimal co-schedules or their lower bounds in scenarios with or without job migrations. For complex cases, it empirically demonstrates the feasibility for approximating the optimal effectively by proposing several heuristics-based algorithms. These discoveries may facilitate the assessment of job co-schedulers by providing necessary baselines, as well as shed insights to the development of co-scheduling algorithms in practical systems.

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Index Terms:
Co-scheduling, shared cache, CMP scheduling, cache contention, perfect matching, integer programming.
Citation:
Yunlian Jiang, Kai Tian, Xipeng Shen, Jinghe Zhang, Jie Chen, Rahul Tripathi, "The Complexity of Optimal Job Co-Scheduling on Chip Multiprocessors and Heuristics-Based Solutions," IEEE Transactions on Parallel and Distributed Systems, vol. 22, no. 7, pp. 1192-1205, July 2011, doi:10.1109/TPDS.2010.193
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