Results 1  10
of
13
Optimal PostRouting Redundant Via Insertion ∗
"... Redundant via insertion is highly recommended for improving chip yield and reliability. In this paper, we study the problem of doublecut via insertion (DVI) in a postrouting stage, where a single via can have at most one redundant via inserted next to it and the goal is to insert as many redundant ..."
Abstract

Cited by 4 (1 self)
 Add to MetaCart
(Show Context)
Redundant via insertion is highly recommended for improving chip yield and reliability. In this paper, we study the problem of doublecut via insertion (DVI) in a postrouting stage, where a single via can have at most one redundant via inserted next to it and the goal is to insert as many redundant vias as possible. The DVI problem can be naturally formulated as a zeroone integer linear program (01 ILP). Our main contributions are acceleration methods for reducing the problem size and the number of constraints. Moreover, we extend the 01 ILP formulation to handle via density constraints. Experimental results show that our 01 ILP is very efficient in computing optimal DVI solution, with up to 35.3 times speedup over existing heuristic algorithms.
Fast and Optimal Redundant Via Insertion
"... Abstract—Redundant via insertion is highly effective in improving chip yield and reliability. In this paper, we study the problem of doublecut via insertion (DVI) in a postrouting stage, where a single via can have, at most, one redundant via inserted next to it and the goal is to insert as many re ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
(Show Context)
Abstract—Redundant via insertion is highly effective in improving chip yield and reliability. In this paper, we study the problem of doublecut via insertion (DVI) in a postrouting stage, where a single via can have, at most, one redundant via inserted next to it and the goal is to insert as many redundant vias as possible. The DVI problem can be naturally formulated as a zero–one integer linear program (0–1 ILP). Our main contributions are acceleration methods for reducing the problem size and the number of constraints. Moreover, we extend the 0–1 ILP formulation to handle via density constraints. Experimental results show that our 0–1 ILP is very efficient in computing an optimal DVI solution, with up to 73.98 times speedup over existing heuristic algorithms. Index Terms—Integer linear program (ILP), redundant via insertion, via density. I.
Distributed Approximation in a Constant Number of Rounds
"... We present a novel efficient distributed approximation algorithm for covering and packing linear programs (LP). We show that on general graphs, for this class of LPs, a nontrivial approximation ratio can be achieved. Namely, for k ∈ O(log(ρ∆)), our (deterministic) algorithm achieves a (ρ∆) 1/kappr ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
(Show Context)
We present a novel efficient distributed approximation algorithm for covering and packing linear programs (LP). We show that on general graphs, for this class of LPs, a nontrivial approximation ratio can be achieved. Namely, for k ∈ O(log(ρ∆)), our (deterministic) algorithm achieves a (ρ∆) 1/kapproximation in O(k 2) rounds, using only messages of size O(log(ρ∆)). Increasing k, we get an (1 + ε)approximation in time O(log 2 (ρ∆)/ε 4) and therefore only log 2 rounds are needed in order to get a constant approximation ratio. Additionally, we show that by combining our LP algorithm with randomized rounding techniques, we obtain efficient distributed approximation algorithms for a number of combinatorial problems. 1 Introduction and Related Work Achieving a global goal based on local information only is one of the key challenges when developing fast distributed algorithms. In k rounds of communication, a network node can only gather information about nodes which are at most k hops away. Not surprisingly, many global criteria such as obtaining a spanning tree cannot be met by a local algorithm, i.e., by an algorithm whose time complexity is much smaller than
Distributed Approximation in a Constant Number of Rounds
"... Abstract We present a novel efficient distributed approximation algorithm for covering and packing linearprograms (LP). We show that on general graphs, for this class of LPs, a nontrivial approximation ratio can be achieved. Namely, for k 2 O(log(ae\Delta)), our (deterministic) algorithm achieves a ..."
Abstract
 Add to MetaCart
Abstract We present a novel efficient distributed approximation algorithm for covering and packing linearprograms (LP). We show that on general graphs, for this class of LPs, a nontrivial approximation ratio can be achieved. Namely, for k 2 O(log(ae\Delta)), our (deterministic) algorithm achieves a (ae\Delta)1/kapproximation in O( k2) rounds, using only messages of size O(log(ae\Delta)). Increasing k, we get an(1 + ")approximation in time O(log2(ae\Delta)/"4) and therefore only log2 rounds are needed in order toget a constant approximation ratio. Additionally, we show that by combining our LP algorithm with randomized rounding techniques, we obtain efficient distributed approximation algorithms for a numberof combinatorial problems. 1 Introduction and Related Work Achieving a global goal based on local information only is one of the key challenges when developing fastdistributed algorithms. In
Distributed Combinatorial Optimization (Extended Abstract)
"... Approximating integer linear programs by solving a relaxation to a linear program (LP) and afterwards reconstructing an integer solution from the fractional one is a standard technique in a nondistributed scenario. Surprisingly, the method has not often been applied for distributed algorithms. In t ..."
Abstract
 Add to MetaCart
(Show Context)
Approximating integer linear programs by solving a relaxation to a linear program (LP) and afterwards reconstructing an integer solution from the fractional one is a standard technique in a nondistributed scenario. Surprisingly, the method has not often been applied for distributed algorithms. In this paper, we show that LP relaxation is a powerful technique also to obtain fast distributed approximation algorithms. We present a novel deterministic distributed algorithm which computes a constant factor approximates for fractional covering and packing problems in only log rounds, using messages of logarithmic size. If messages are allowed to be larger, we show that a constant approximation can be achieved in a logarithmic number of rounds only. Finally, we show that by combining our LP approximation algorithms with randomized rounding techniques, we obtain efficient distributed approximation algorithms for a number of combinatorial problems.
ACM SIGACT news distributed computing column 5
 SIGACT News
"... The Distributed Computing Column covers the theory of systems that are composed of a number of interacting computing elements. These include problems of communication and networking, databases, distributed shared memory, multiprocessor architectures, operating systems, verification, Internet, and th ..."
Abstract
 Add to MetaCart
(Show Context)
The Distributed Computing Column covers the theory of systems that are composed of a number of interacting computing elements. These include problems of communication and networking, databases, distributed shared memory, multiprocessor architectures, operating systems, verification, Internet, and the Web. This issue consists of: • “Travelling through Wormholes: a new look at Distributed Systems Models, ” by Paulo E. Veríssimo. Many thanks to Paulo for his contribution to this issue. Request for Collaborations: Please send me any suggestions for material I should be including in this column, including news and communications, open problems, and authors willing to write a guest column or to review an event related to theory of distributed computing. Travelling through Wormholes: a new look at Distributed Systems Models