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Applying parallel computation algorithms in the design of serial algorithms
 J. ACM
, 1983
"... Abstract. The goal of this paper is to point out that analyses of parallelism in computational problems have practical implications even when multiprocessor machines are not available. This is true because, in many cases, a good parallel algorithm for one problem may turn out to be useful for design ..."
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Cited by 238 (7 self)
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Abstract. The goal of this paper is to point out that analyses of parallelism in computational problems have practical implications even when multiprocessor machines are not available. This is true because, in many cases, a good parallel algorithm for one problem may turn out to be useful for designing an efficient serial algorithm for another problem. A d ~ eframework d for cases like this is presented. Particular cases, which are discussed in this paper, provide motivation for examining parallelism in sorting, selection, minimumspanningtree, shortest route, maxflow, and matrix multiplication problems, as well as in scheduling and locational problems.
A cacheaware parallel implementation of the pushrelabel network flow algorithm and experimental evaluation of the gap relabeling heuristic
 In Proc. 18th intl. conf. on Parallel and Distributed Computing Systems
, 2005
"... The maximum flow problem is a combinatorial problem of significant importance in a wide variety of research and commercial applications. It has been extensively studied and implemented over the past 40 years. The pushrelabel method has been shown to be superior to other methods, both in theoretica ..."
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Cited by 12 (0 self)
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The maximum flow problem is a combinatorial problem of significant importance in a wide variety of research and commercial applications. It has been extensively studied and implemented over the past 40 years. The pushrelabel method has been shown to be superior to other methods, both in theoretical bounds and in experimental implementations. Our study discusses the implementation of the pushrelabel network flow algorithm on presentday symmetric multiprocessors (SMP’s) with large shared memories. The maximum flow problem is an irregular graph problem and requires frequent finegrained locking of edges and vertices. Over a decade ago, Anderson and Setubal implemented Goldberg’s pushrelabel algorithm for shared memory parallel computers; however, modern systems differ significantly from those targeted by their implementation in that SMP’s today have deep memory hierarchies and different performance costs for synchronization and finegrained locking. Besides our new cacheaware implementation of Goldberg’s parallel algorithm for modern sharedmemory parallel computers, our main new contribution is the first parallel implementation and analysis of the gap relabeling heuristic that runs from 2.1 to 4.3 times faster for sparse graphs.
A CacheAware Parallel Implementation of the PushRelabel Network Flow Algorithm and Experimental Evaluation of the Gap Relabeling Heuristic
"... The maximum flow problem is a combinatorial problem of significant importance in a wide variety of research and commercial applications. It has been extensively studied and implemented over the past 40 years. The pushrelabel method has been shown to be superior to other methods, both in theoretical ..."
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The maximum flow problem is a combinatorial problem of significant importance in a wide variety of research and commercial applications. It has been extensively studied and implemented over the past 40 years. The pushrelabel method has been shown to be superior to other methods, both in theoretical bounds and in experimental implementations. Our study discusses the implementation of the pushrelabel network flow algorithm on presentday symmetric multiprocessors (SMP’s) with large shared memories. The maximum flow problem is an irregular graph problem and requires frequent finegrained locking of edges and vertices. Over a decade ago, Anderson and Setubal implemented Goldberg’s pushrelabel algorithm for shared memory parallel computers; however, modern systems differ significantly from those targeted by their implementation in that SMP’s today have deep memory hierarchies and different performance costs for synchronization and finegrained locking. Besides our new cacheaware implementation of Goldberg’s parallel algorithm for modern sharedmemory parallel computers, our main new contribution is the first parallel implementation and analysis of the gap relabeling heuristic that runs from 2.1 to 4.3 times faster for sparse graphs. 1