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22
Adaptive Functional Programming
 IN PROCEEDINGS OF THE 29TH ANNUAL ACM SYMPOSIUM ON PRINCIPLES OF PROGRAMMING LANGUAGES
, 2001
"... An adaptive computation maintains the relationship between its input and output as the input changes. Although various techniques for adaptive computing have been proposed, they remain limited in their scope of applicability. We propose a general mechanism for adaptive computing that enables one to ..."
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Cited by 78 (27 self)
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An adaptive computation maintains the relationship between its input and output as the input changes. Although various techniques for adaptive computing have been proposed, they remain limited in their scope of applicability. We propose a general mechanism for adaptive computing that enables one to make any purelyfunctional program adaptive. We show
An experimental analysis of selfadjusting computation
 In Proceedings of the ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI
, 2006
"... Selfadjusting computation uses a combination of dynamic dependence graphs and memoization to efficiently update the output of a program as the input changes incrementally or dynamically over time. Related work showed various theoretical results, indicating that the approach can be effective for a r ..."
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Cited by 47 (23 self)
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Selfadjusting computation uses a combination of dynamic dependence graphs and memoization to efficiently update the output of a program as the input changes incrementally or dynamically over time. Related work showed various theoretical results, indicating that the approach can be effective for a reasonably broad range of applications. In this article, we describe algorithms and implementation techniques to realize selfadjusting computation and present an experimental evaluation of the proposed approach on a variety of applications, ranging from simple list primitives to more sophisticated computational geometry algorithms. The results of the experiments show that the approach is effective in practice, often offering orders of magnitude speedup from recomputing the output from scratch. We believe this is the first experimental evidence that incremental computation of any type is effective in practice for a reasonably broad set of applications.
CEAL: a Cbased language for selfadjusting computation
 In ACM SIGPLAN Conference on Programming Language Design and Implementation
, 2009
"... Selfadjusting computation offers a languagecentric approach to writing programs that can automatically respond to modifications to their data (e.g., inputs). Except for several domainspecific implementations, however, all previous implementations of selfadjusting computation assume mostly functi ..."
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Cited by 23 (10 self)
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Selfadjusting computation offers a languagecentric approach to writing programs that can automatically respond to modifications to their data (e.g., inputs). Except for several domainspecific implementations, however, all previous implementations of selfadjusting computation assume mostly functional, higherorder languages such as Standard ML. Prior to this work, it was not known if selfadjusting computation can be made to work with lowlevel, imperative languages such as C without placing undue burden on the programmer. We describe the design and implementation of CEAL: a Cbased language for selfadjusting computation. The language is fully general and extends C with a small number of primitives to enable writing selfadjusting programs in a style similar to conventional C programs. We present efficient compilation techniques for translating CEAL programs into C that can be compiled with existing C compilers using primitives supplied by a runtime library for selfadjusting computation. We implement the proposed compiler and evaluate its effectiveness. Our experiments show that CEAL is effective in practice: compiled selfadjusting programs respond to small modifications to their data by orders of magnitude faster than recomputing from scratch while slowing down a fromscratch run by a moderate constant factor. Compared to previous work, we
Dynamic parallel tree contraction
 In Proceedings 5th Annual ACM Symp. on Parallel Algorithms and Architectures
, 1994
"... Parallel tree contraction has been found to be a useful and quite powerful tool for the design of a wide class of efficient graph algorithms. We propose a corresponding technique for the parallel solution of problems with incremental changes in the data. In dynamic tree contraction problems, we are ..."
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Cited by 18 (2 self)
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Parallel tree contraction has been found to be a useful and quite powerful tool for the design of a wide class of efficient graph algorithms. We propose a corresponding technique for the parallel solution of problems with incremental changes in the data. In dynamic tree contraction problems, we are given an initial tree T, and then an online algorithm processes requests regarding T. Requests may be either incremental changes to T or requests for certain values computed using the tree. A simple example is maintaining the preorder numbering on a tree. The online algorithm would then have to handle incremental changes to the tree, and would also have to quickly answer queries about the preorder number of any tree node. Our dynamic algorithms are based on the prior parallel tree contraction algorithms, and hence we call such algorithms incremental tree contraction algorithms. By maintaining the connection between our incremental algorithms and the parallel tree contraction algorithm, we create incremental algorithms for tree contraction. We consider a dynamic binary tree T of ≤ n nodes and unbounded depth. We describe a procedure, which we call the dynamic parallel tree contraction algorithm, which incrementally processes various parallel modification requests and queries: (1) parallel requests to add or delete leaves of T, or modify labels of internal nodes or leaves of T, and also (2) parallel tree contraction queries which require recomputing values at specified nodes. Each modification or query is with respect to a set of nodes U in T. We make use of a random splitting tree as an aid
Adaptive Bayesian inference
 In Proc. NIPS
, 2008
"... Motivated by stochastic systems in which observed evidence and conditional dependencies between states of the network change over time, and certain quantities of interest (marginal distributions, likelihood estimates etc.) must be updated, we study the problem of adaptive inference in treestructure ..."
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Cited by 11 (6 self)
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Motivated by stochastic systems in which observed evidence and conditional dependencies between states of the network change over time, and certain quantities of interest (marginal distributions, likelihood estimates etc.) must be updated, we study the problem of adaptive inference in treestructured Bayesian networks. We describe an algorithm for adaptive inference that handles a broad range of changes to the network and is able to maintain marginal distributions, MAP estimates, and data likelihoods in all expected logarithmic time. We give an implementation of our algorithm and provide experiments that show that the algorithm can yield up to two orders of magnitude speedups on answering queries and responding to dynamic changes over the sumproduct algorithm. 1
A consistent semantics of selfadjusting computation
, 2006
"... Abstract. This paper presents a semantics of selfadjusting computation and proves that the semantics is correct and consistent. The semantics integrates change propagation with the classic idea of memoization to enable reuse of computations under mutation to memory. During evaluation, reuse of a co ..."
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Cited by 9 (8 self)
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Abstract. This paper presents a semantics of selfadjusting computation and proves that the semantics is correct and consistent. The semantics integrates change propagation with the classic idea of memoization to enable reuse of computations under mutation to memory. During evaluation, reuse of a computation via memoization triggers a change propagation that adjusts the reused computation to reflect the mutated memory. Since the semantics combines memoization and changepropagation, it involves both nondeterminism and mutation. Our consistency theorem states that the nondeterminism is not harmful: any two evaluations of the same program starting at the same state yield the same result. Our correctness theorem states that mutation is not harmful: selfadjusting programs are consistent with purely functional programming. We formalized the semantics and its metatheory in the LF logical framework and machinechecked the proofs in Twelf. 1
Dynamic trees in practice
 ACM J. Exp. Algor
, 2009
"... Dynamic tree data structures maintain forests that change over time through edge insertions and deletions. Besides maintaining connectivity information in logarithmic time, they can support aggregation of information over paths, trees, or both. We perform an experimental comparison of several versio ..."
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Cited by 8 (3 self)
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Dynamic tree data structures maintain forests that change over time through edge insertions and deletions. Besides maintaining connectivity information in logarithmic time, they can support aggregation of information over paths, trees, or both. We perform an experimental comparison of several versions of dynamic trees: STtrees, ETtrees, RCtrees, and two variants of top trees (selfadjusting and worstcase). We quantify their strengths and weaknesses through tests with various workloads, most stemming from practical applications. We observe that a simple, lineartime implementation is remarkably fast for graphs of small diameter, and that worstcase and randomized data structures are best when queries are very frequent. The best overall performance, however, is achieved by selfadjusting STtrees.
Adaptive Inference on General Graphical Models
"... Many algorithms and applications involve repeatedly solving variations of the same inference problem; for example we may want to introduce new evidence to the model or perform updates to conditional dependencies. The goal of adaptive inference is to take advantage of what is preserved in the model a ..."
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Cited by 7 (0 self)
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Many algorithms and applications involve repeatedly solving variations of the same inference problem; for example we may want to introduce new evidence to the model or perform updates to conditional dependencies. The goal of adaptive inference is to take advantage of what is preserved in the model and perform inference more rapidly than from scratch. In this paper, we describe techniques for adaptive inference on general graphs that support marginal computation and updates to the conditional probabilities and dependencies in logarithmic time. We give experimental results for an implementation of our algorithm, and demonstrate its potential performance benefit in the study of protein structure. 1
Design and Analysis of Data Structures for Dynamic Trees
, 2006
"... The dynamic trees problem is that of maintaining a forest that changes over time through edge insertions and deletions. We can associate data with vertices or edges and manipulate this data, individually or in bulk, with operations that deal with whole paths or trees. Efficient solutions to this pr ..."
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Cited by 6 (1 self)
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The dynamic trees problem is that of maintaining a forest that changes over time through edge insertions and deletions. We can associate data with vertices or edges and manipulate this data, individually or in bulk, with operations that deal with whole paths or trees. Efficient solutions to this problem have numerous applications, particularly in algorithms for network flows and dynamic graphs in general. Several data structures capable of logarithmictime dynamic tree operations have been proposed. The first was Sleator and Tarjan’s STtree, which represents a partition of the tree into paths. Although reasonably fast in practice, adapting STtrees to different applications is nontrivial. Frederickson’s topology trees, Alstrup et al.’s top trees, and Acar et al.’s RCtrees are based on tree contractions: they progressively combine vertices or edges to obtain a hierarchical representation of the tree. This approach is more flexible in theory, but all known implementations assume the trees have bounded degree; arbitrary trees are supported only after ternarization. This thesis shows how these two approaches can be combined (with very little overhead) to produce a data structure that is at least as generic as any other, very easy to
Fast Parallel and Adaptive Updates for DualDecomposition Solvers

, 2011
"... Dualdecomposition (DD) methods are quickly becoming important tools for estimating the minimum energy state of a graphical model. DD methods decompose a complex model into a collection of simpler subproblems that can be solved exactly (such as trees), that in combination provide upper and lower bou ..."
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Cited by 3 (3 self)
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Dualdecomposition (DD) methods are quickly becoming important tools for estimating the minimum energy state of a graphical model. DD methods decompose a complex model into a collection of simpler subproblems that can be solved exactly (such as trees), that in combination provide upper and lower bounds on the exact solution. Subproblem choice can play a major role: larger subproblems tend to improve the bound more per iteration, while smaller subproblems enable highly parallel solvers and can benefit from reusing past solutions when there are few changes between iterations. We propose an algorithm that can balance many of these aspects to speed up convergence. Our method uses a cluster tree data structure that has been proposed for adaptive exact inference tasks, and we apply it in this paper to dualdecomposition approximate inference. This approach allows us to process large subproblems to improve the bounds at each iteration, while allowing a high degree of parallelizability and taking advantage of subproblems with sparse updates. For both synthetic inputs and a realworld stereo matching problem, we demonstrate that our algorithm is able to achieve significant improvement in convergence time.