TY - GEN
T1 - Power grid analysis with hierarchical support graphs
AU - Zhao, Xueqian
AU - Wang, Jia
AU - Feng, Zhuo
AU - Hu, Shiyan
PY - 2011
Y1 - 2011
N2 - It is increasingly challenging to analyze present day large-scale power delivery networks (PDNs) due to the drastically growing complexity in power grid design. To achieve greater runtime and memory efficiencies, a variety of preconditioned iterative algorithms has been investigated in the past few decades with promising performance, while incremental power grid analysis also becomes popular to facilitate fast re-simulations of corrected designs. Although existing preconditioned solvers, such as incomplete matrix factor-based preconditioners, usually exhibit high efficiency in memory usage, their convergence behaviors are not always satisfactory. In this work, we present a novel hierarchical support-graph preconditioned iterative algorithm that constructs preconditioners by generating spanning trees in power supply networks for fast power grid analysis. The support-graph preconditioner is efficient for handling complex power grid structures (regular or irregular grids), and can facilitate very fast incremental analysis. Our experimental results on IBM power grid benchmarks show that compared with the best direct or iterative solvers, the proposed support-graph preconditioned iterative solver achieves up to 3.6X speedups for DC analysis, and up to 22X speedups for incremental analysis, while reducing the memory consumption by a factor of four.
AB - It is increasingly challenging to analyze present day large-scale power delivery networks (PDNs) due to the drastically growing complexity in power grid design. To achieve greater runtime and memory efficiencies, a variety of preconditioned iterative algorithms has been investigated in the past few decades with promising performance, while incremental power grid analysis also becomes popular to facilitate fast re-simulations of corrected designs. Although existing preconditioned solvers, such as incomplete matrix factor-based preconditioners, usually exhibit high efficiency in memory usage, their convergence behaviors are not always satisfactory. In this work, we present a novel hierarchical support-graph preconditioned iterative algorithm that constructs preconditioners by generating spanning trees in power supply networks for fast power grid analysis. The support-graph preconditioner is efficient for handling complex power grid structures (regular or irregular grids), and can facilitate very fast incremental analysis. Our experimental results on IBM power grid benchmarks show that compared with the best direct or iterative solvers, the proposed support-graph preconditioned iterative solver achieves up to 3.6X speedups for DC analysis, and up to 22X speedups for incremental analysis, while reducing the memory consumption by a factor of four.
UR - http://www.scopus.com/inward/record.url?scp=84862948892&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84862948892&partnerID=8YFLogxK
U2 - 10.1109/ICCAD.2011.6105383
DO - 10.1109/ICCAD.2011.6105383
M3 - Conference contribution
AN - SCOPUS:84862948892
SN - 9781457713989
T3 - IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
SP - 543
EP - 547
BT - 2011 IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2011
T2 - 2011 IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2011
Y2 - 7 November 2011 through 10 November 2011
ER -