TY - GEN
T1 - Approximation algorithm for the temperature-aware scheduling problem
AU - Zhang, Sushu
AU - Chatha, Karam S.
PY - 2007/12/1
Y1 - 2007/12/1
N2 - The paper addresses the problem of performance optimization for a set of periodic tasks with discrete voltage/frequency states under thermal constraints. We prove that the problem is NP-hard, and present a pseudo-polynomial optimal algorithm and a fully polynomial time approximation technique (FPTAS) for the problem. The FPTAS technique is able to generate solutions in polynomial time that are guaranteed to be within a designer specified quality bound (QB) (say within 1% of the optimal). We evaluate our techniques by experimentation with multimedia and synthetic benchmarks mapped on the 70nm CMOS technology processor. The experimental results demonstrate our techniques are able to match optimal solutions when QB is set at 5%, can generate solutions that are quite close to optimal (< 5%) even when QB is set at higher values (50%), and executes in few seconds (with QB > 25%) for large task sets with 120 nodes (while the optimal solution takes several hundred seconds). We also analyze the effect of different thermal parameters, such as the initial temperature, the final temperature and the thermal resistance.
AB - The paper addresses the problem of performance optimization for a set of periodic tasks with discrete voltage/frequency states under thermal constraints. We prove that the problem is NP-hard, and present a pseudo-polynomial optimal algorithm and a fully polynomial time approximation technique (FPTAS) for the problem. The FPTAS technique is able to generate solutions in polynomial time that are guaranteed to be within a designer specified quality bound (QB) (say within 1% of the optimal). We evaluate our techniques by experimentation with multimedia and synthetic benchmarks mapped on the 70nm CMOS technology processor. The experimental results demonstrate our techniques are able to match optimal solutions when QB is set at 5%, can generate solutions that are quite close to optimal (< 5%) even when QB is set at higher values (50%), and executes in few seconds (with QB > 25%) for large task sets with 120 nodes (while the optimal solution takes several hundred seconds). We also analyze the effect of different thermal parameters, such as the initial temperature, the final temperature and the thermal resistance.
UR - http://www.scopus.com/inward/record.url?scp=50249139685&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=50249139685&partnerID=8YFLogxK
U2 - 10.1109/ICCAD.2007.4397278
DO - 10.1109/ICCAD.2007.4397278
M3 - Conference contribution
AN - SCOPUS:50249139685
SN - 1424413826
SN - 9781424413829
T3 - IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
SP - 281
EP - 288
BT - 2007 IEEE/ACM International Conference on Computer-Aided Design, ICCAD
T2 - 2007 IEEE/ACM International Conference on Computer-Aided Design, ICCAD
Y2 - 4 November 2007 through 8 November 2007
ER -