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Practical Approximation Algorithms for Separable Packing Linear Programs

Authors: Feodor F. Dragan; Andrew B. Kahng; Ion I. Măndoiu; Sudhakar Muddu; Alexander Zelikovsky;

Practical Approximation Algorithms for Separable Packing Linear Programs

Abstract

We describe fully polynomial time approximation schemes for generalized multicommodity flow problems arising in VLSI applications such as Global Routing via Buffer Blocks (GRBB). We extend Fleischer's improvement [7] of Garg and Konemann [8] fully polynomial time approximation scheme for edge capacitated multicommodity flows to multiterminal multicommodity flows in graphs with capacities on vertices and subsets of vertices. In addition, our problem formulations observe upper bounds and parity constraints on the number of vertices on any source-to-sink path. Unlike previous works on the GRBB problem [5,17], our algorithms can take into account (i) multiterminal nets, (ii) simultaneous buffered routing and compaction, and (iii) buffer libraries. Our method outperforms existing algorithms for the problem and has been validated on top-level layouts extracted from a recent high-end microprocessor design.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Average
Average
Average