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284
On the Dynamic Finger Conjecture for Splay Trees. Part II: The Proof
 SIAM Journal on Computing
"... The following result is shown: On an nnode splay tree, the amortized cost of an access at distance d from the preceding access is O(log(d + 1)). In addition, there is an O(n) initialization cost. The accesses include searches, insertions and deletions. 1 Introduction The reader is advised that ..."
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Cited by 45 (1 self)
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The following result is shown: On an nnode splay tree, the amortized cost of an access at distance d from the preceding access is O(log(d + 1)). In addition, there is an O(n) initialization cost. The accesses include searches, insertions and deletions. 1 Introduction The reader is advised that this paper quotes results from the companion Part I paper [CMSS93]; in addition, the Part I paper introduces a number of the techniques used here, but in a somewhat less involved way. The splay tree is a selfadjusting binary search tree devised by Sleator and Tarjan [ST85]. They showed that it is competitive with many of the balanced search tree schemes for maintaining a dictionary. Specifically, Sleator and Tarjan showed that a sequence of m accesses performed on a splay tree takes time O(m log n), where n is the maximum size attained by the tree (n m). They also showed that in an amortized sense, up to a constant factor, on sufficiently long sequences of searches, the splay tree has as ...
Improved Algorithms For Bipartite Network Flow
, 1994
"... In this paper, we study network flow algorithms for bipartite networks. A network G = (V; E) is called bipartite if its vertex set V can be partitioned into two subsets V 1 and V 2 such that all edges have one endpoint in V 1 and the other in V 2 . Let n = jV j, n 1 = jV 1 j, n 2 = jV 2 j, m = jE ..."
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Cited by 44 (7 self)
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In this paper, we study network flow algorithms for bipartite networks. A network G = (V; E) is called bipartite if its vertex set V can be partitioned into two subsets V 1 and V 2 such that all edges have one endpoint in V 1 and the other in V 2 . Let n = jV j, n 1 = jV 1 j, n 2 = jV 2 j, m = jEj and assume without loss of generality that n 1 n 2 . We call a bipartite network unbalanced if n 1 ø n 2 and balanced otherwise. (This notion is necessarily imprecise.) We show that several maximum flow algorithms can be substantially sped up when applied to unbalanced networks. The basic idea in these improvements is a twoedge push rule that allows us to "charge" most computation to vertices in V 1 , and hence develop algorithms whose running times depend on n 1 rather than n. For example, we show that the twoedge push version of Goldberg and Tarjan's FIFO preflow push algorithm runs in O(n 1 m + n 3 1 ) time and that the analogous version of Ahuja and Orlin's excess scaling algori...
A Comparative Performance Evaluation of Write Barrier Implementations
, 1992
"... Generational garbage collectors are able to achieve very small pause times by concentrating on the youngest (most recently allocated) objects when collecting, since objects have been observed to die young in many systems. Generational collectors must keep track of all pointers from older to younger ..."
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Cited by 42 (12 self)
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Generational garbage collectors are able to achieve very small pause times by concentrating on the youngest (most recently allocated) objects when collecting, since objects have been observed to die young in many systems. Generational collectors must keep track of all pointers from older to younger generations, by "monitoring " all stores into the heap. This write barrier has been implemented in a number of ways, varying essentially in the granularity of the information observed and stored. Here we examine a range of write barrier implementations and evaluate their relative performance within a generation scavenging garbage collector for Smalltalk.
Practical, transparent operating system support for superpages
 SIGOPS Oper. Syst. Rev
, 2002
"... Most generalpurpose processors provide support for memory pages of large sizes, called superpages. Superpages enable each entry in the translation lookaside buffer (TLB) to map a large physical memory region into a virtual address space. This dramatically increases TLB coverage, reduces TLB misses, ..."
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Cited by 39 (4 self)
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Most generalpurpose processors provide support for memory pages of large sizes, called superpages. Superpages enable each entry in the translation lookaside buffer (TLB) to map a large physical memory region into a virtual address space. This dramatically increases TLB coverage, reduces TLB misses, and promises performance improvements for many applications. However, supporting superpages poses several challenges to the operating system, in terms of superpage allocation and promotion tradeoffs, fragmentation control, etc. We analyze these issues, and propose the design of an effective superpage management system. We implement it in FreeBSD on the Alpha CPU, and evaluate it on real workloads and benchmarks. We obtain substantial performance benefits, often exceeding 30%; these benefits are sustained even under stressful workload scenarios. 1
A 1.5Approximation Algorithm for Sorting by Transpositions and Transreversals
, 2003
"... One of the most promising ways to determine evolutionary distance between two organisms is to compare the order of appearance of orthologous genes in their genomes. The resulting genome rearrangement problem calls for finding a shortest sequence of rearrangement operations that sorts one genome into ..."
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Cited by 38 (3 self)
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One of the most promising ways to determine evolutionary distance between two organisms is to compare the order of appearance of orthologous genes in their genomes. The resulting genome rearrangement problem calls for finding a shortest sequence of rearrangement operations that sorts one genome into the other. In this paper we provide a 1.5approximation algorithm for the problem of sorting by transpositions and transreversals, improving on a fiveyearsold 1.75 ratio for this problem. Our algorithm is also faster than current approaches and requires O(n # log n) time for n genes.
Focusing the inverse method for linear logic
 Proceedings of CSL 2005
, 2005
"... 1.1 Quantification and the subformula property.................. 3 1.2 Ground forward sequent calculus......................... 5 1.3 Lifting to free variables............................... 10 ..."
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Cited by 38 (11 self)
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1.1 Quantification and the subformula property.................. 3 1.2 Ground forward sequent calculus......................... 5 1.3 Lifting to free variables............................... 10
AN O(n log log n)TIME ALGORITHM FOR TRIANGULATING A SIMPLE POLYGON
, 1988
"... Given a simple nvertex polygon, the triangulation problem is to partition the interior of the polygon into n2 triangles by adding n3 nonintersecting diagonals. We propose an O(n log logn)time algorithm for this problem, improving on the previously best bound of O (n log n) and showing that tria ..."
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Cited by 37 (4 self)
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Given a simple nvertex polygon, the triangulation problem is to partition the interior of the polygon into n2 triangles by adding n3 nonintersecting diagonals. We propose an O(n log logn)time algorithm for this problem, improving on the previously best bound of O (n log n) and showing that triangulation is not as hard as sorting. Improved algorithms for several other computational geometry problems, including testing whether a polygon is simple, follow from our result.
Geometric Algorithms for Online Optimization
 Journal of Computer and System Sciences
, 2002
"... In this paper, we consider a natural online version of linear optimization: the problem has to be solved repeatedly over a sequence of periods, where the objective direction for the upcoming period is unknown. This models online versions of optimization problems, such as maxcut, variants of cluster ..."
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Cited by 35 (1 self)
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In this paper, we consider a natural online version of linear optimization: the problem has to be solved repeatedly over a sequence of periods, where the objective direction for the upcoming period is unknown. This models online versions of optimization problems, such as maxcut, variants of clustering, and also the classic online binary search tree problem. We present algorithms for this problem that are (1 + epsilon)competitive with the optimal static solution chosen in hindsight. Our algorithms and proofs are motivated by geometric considerations.