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Bureaucratic protocols for secure twoparty sorting, selection, and permuting
 In 5th ACM Symposium on Information, Computer and Communications Security (ASIACCS
, 2010
"... In this paper, we introduce a framework for secure twoparty (S2P) computations, which we call bureaucratic computing, and we demonstrate its efficiency by designing practical S2P computations for sorting, selection, and random permutation. In a nutshell, the main idea behind bureaucratic computing ..."
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In this paper, we introduce a framework for secure twoparty (S2P) computations, which we call bureaucratic computing, and we demonstrate its efficiency by designing practical S2P computations for sorting, selection, and random permutation. In a nutshell, the main idea behind bureaucratic computing is to design dataoblivious algorithms that push all knowledge and influence of input values down to small blackbox circuits, which are simulated using Yao’s garbled paradigm. The practical benefit of this approach is that it maintains the zeroknowledge features of secure twoparty computations while avoiding the significant computational overheads that come from trying to apply Yao’s garbled paradigm to anything other than simple twoinput
AverageCase Analysis of Algorithms Using Kolmogorov Complexity
 JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
, 2000
"... Analyzing the averagecase complexity of algorithms is a very practical but very difficult problem in computer science. In the past few years, we have demonstrated that Kolmogorov complexity is an important tool for analyzing the averagecase complexity of algorithms. We have developed the incomp ..."
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Analyzing the averagecase complexity of algorithms is a very practical but very difficult problem in computer science. In the past few years, we have demonstrated that Kolmogorov complexity is an important tool for analyzing the averagecase complexity of algorithms. We have developed the incompressibility method [7]. In this paper, we use several simple examples to further demonstrate the power and simplicity of such method. We prove bounds on the averagecase number of stacks (queues) required for sorting sequential or parallel Queueusort or Stacksort.
Generic Discrimination  Sorting and Partitioning Unshared Data in Linear Time
, 2008
"... We introduce the notion of discrimination as a generalization of both sorting and partitioning and show that worstcase lineartime discrimination functions (discriminators) can be defined generically, by (co)induction on an expressive language of order denotations. The generic definition yields di ..."
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We introduce the notion of discrimination as a generalization of both sorting and partitioning and show that worstcase lineartime discrimination functions (discriminators) can be defined generically, by (co)induction on an expressive language of order denotations. The generic definition yields discriminators that generalize both distributive sorting and multiset discrimination. The generic discriminator can be coded compactly using list comprehensions, with order denotations specified using Generalized Algebraic Data Types (GADTs). A GADTfree combinator formulation of discriminators is also given. We give some examples of the uses of discriminators, including a new mostsignificantdigit lexicographic sorting algorithm. Discriminators generalize binary comparison functions: They operate on n arguments at a time, but do not expose more information than the underlying equivalence, respectively ordering relation on the arguments. We argue that primitive types with equality (such as references in ML) and ordered types (such as the machine integer type), should expose their equality, respectively standard ordering relation, as discriminators: Having only a binary equality test on a type requires Θ(n 2) time to find all the occurrences of an element in a list of length n, for each element in the list, even if the equality test takes only constant time. A discriminator accomplishes this in linear time. Likewise, having only a (constanttime) comparison function requires Θ(n log n) time to sort a list of n elements. A discriminator can do this in linear time.
Auburn: A Kit for Benchmarking Functional Data Structures
 LECTURE NOTES IN COMPUTER SCIENCE
, 1997
"... Benchmarking competing implementations of a data structure can be both tricky and time consuming. The efficiency of an implementation may depend critically on how it is used. This problem is compounded by persistence. All purely functional data structures are persistent. We present a kit that c ..."
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Benchmarking competing implementations of a data structure can be both tricky and time consuming. The efficiency of an implementation may depend critically on how it is used. This problem is compounded by persistence. All purely functional data structures are persistent. We present a kit that can generate benchmarks for a given data structure. A benchmark is made from a description of how it should use an implementation of the data structure. The kit will improve the speed, ease and power of the process of benchmarking functional data structures.
A mixed evolutionarystatistical analysis of an algorithm’s complexity
 Applied Mathematics Letters
"... Abstract: A combination of evolutionary algorithms and statistical techniques is used to analyze the worstcase computational complexity of two sorting algorithms. It is shown that excellent bounds for these algorithms can be obtained using this approach; this fact raises interesting prospects for a ..."
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Abstract: A combination of evolutionary algorithms and statistical techniques is used to analyze the worstcase computational complexity of two sorting algorithms. It is shown that excellent bounds for these algorithms can be obtained using this approach; this fact raises interesting prospects for applying the approach to other problems and algorithms. Several guidelines for extending this work are included.
Generic topdown discrimination
, 2009
"... We introduce the notion of discrimination as a generalization of both sorting and partitioning and show that discriminators (discrimination functions) can be defined generically, by structural recursion on order and equivalence expressions denoting a rich class of total preorders and equivalence rel ..."
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We introduce the notion of discrimination as a generalization of both sorting and partitioning and show that discriminators (discrimination functions) can be defined generically, by structural recursion on order and equivalence expressions denoting a rich class of total preorders and equivalence relations, respectively. Discriminators improve the asymptotic performance of generic comparisonbased sorting and partitioning, yet do not expose more information than the underlying ordering relation, respectively equivalence. For a large class of order and equivalence expressions, including all standard orders for firstorder recursive types, the discriminators execute in worstcase linear time. The generic discriminators can be coded compactly using list comprehensions, with order expressions specified using Generalized Algebraic Data Types (GADTs). We give some examples of the uses of discriminators, including a new mostsignificantdigit lexicographic sorting algorithm and type isomorphism with an associativecommutative operator. Full source code of discriminators and their applications is included. 1 We argue discriminators should be basic operations for primitive and abstract types with equality. The basic multiset discriminator for references, originally due to Paige et al., is shown to be both efficient and fully abstract: it finds all duplicates of all references occurring in a list in linear time without leaking information about their representation. In particular, it behaves deterministically in the presence of garbage collection and nondeterministic heap allocation even when references are represented as raw machine addresses. In contrast, having only a binary equality test as in ML requires Θ(n 2) time, and allowing hashing for performance reasons as in Java, makes execution nondeterministic and complicates garbage collection.
An Experimental Study of Sorting and Branch Prediction
"... Sorting is one of the most important and well studied problems in Computer Science. Many good algorithms are known which offer various tradeoffs in efficiency, simplicity, memory use, and other factors. However, these algorithms do not take into account features of modern computer architectures tha ..."
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Sorting is one of the most important and well studied problems in Computer Science. Many good algorithms are known which offer various tradeoffs in efficiency, simplicity, memory use, and other factors. However, these algorithms do not take into account features of modern computer architectures that significantly influence performance. Caches and branch predictors are two such features, and while there has been a significant amount of research into the cache performance of general purpose sorting algorithms, there has been little research on their branch prediction properties. In this paper we empirically examine the behaviour of the branches in all the most common sorting algorithms. We also consider the interaction of cache optimization on the predictability of the branches in these algorithms. We find insertion sort to have the fewest branch mispredictions of any comparisonbased sorting algorithm, that bubble and shaker sort operate in a fashion which makes their branches highly unpredictable, that the unpredictability of shellsort’s branches improves its caching behaviour and that several cache optimizations have little effect on mergesort’s branch mispredictions. We find also that optimizations to quicksort – for example the choice of pivot – have a strong influence on the predictability of its branches. We point out a simple way of removing branch instructions from a classic heapsort implementation, and show also that unrolling a loop in a cache optimized heapsort implementation improves the predicitability of its branches. Finally, we note that when sorting random data twolevel adaptive branch predictors are usually no better than simpler bimodal predictors. This is despite the fact that twolevel adaptive predictors are almost always superior to bimodal predictors in general.
Benchmarking Purely Functional Data Structures
 Journal of Functional Programming
, 1999
"... When someone designs a new data structure, they want to know how well it performs. Previously, the only way to do this involves finding, coding and testing some applications to act as benchmarks. This can be tedious and timeconsuming. Worse, how a benchmark uses a data structure may considerably af ..."
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When someone designs a new data structure, they want to know how well it performs. Previously, the only way to do this involves finding, coding and testing some applications to act as benchmarks. This can be tedious and timeconsuming. Worse, how a benchmark uses a data structure may considerably affect the efficiency of the data structure. Thus, the choice of benchmarks may bias the results. For these reasons, new data structures developed for functional languages often pay little attention to empirical performance. We solve these problems by developing a benchmarking tool, Auburn, that can generate benchmarks across a fair distribution of uses. We precisely define "the use of a data structure", upon which we build the core algorithms of Auburn: how to generate a benchmark from a description of use, and how to extract a description of use from an application. We consider how best to use these algorithms to benchmark competing data structures. Finally, we test Auburn by benchmarking ...
Generic Discrimination: Partitioning and Sorting of Complex Data in Linear Time
"... Abstract. We introduce the notion of discrimination, which is a generalized form of partitioning, and present an expressive term language for defining equivalence relations on complex data. The language allows definition of equivalence relations by freely combining structural equivalence, equivalenc ..."
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Abstract. We introduce the notion of discrimination, which is a generalized form of partitioning, and present an expressive term language for defining equivalence relations on complex data. The language allows definition of equivalence relations by freely combining structural equivalence, equivalence of lists under commutativity, idempotence (bag and set equivalence), and more. We then show that worstcase lineartime discriminators can be defined generically, by induction on the term language, using multiset discrimination. By employing discriminators for base types such as characters and integer segments that sort their inputs, it can be shown that the inductive construction yields discriminators that both partition and sort their input in linear time for a wide range of total preorders. This amounts to generically bootstrapping pigeonhole sorting for a finite segment of primitive data to lineartime sorting of complex data. We show how these discriminators, both sorting and nonsorting, can be coded up compactly and elegantly using Generalized Algebraic Data Types (GADTs) and list comprehensions and give some examples of applications of the use of discriminators. Finally, we argue that discrimination should replace equality testing as a language primitive for builtin types and abstract types that wish to only make equality observable: they algorithmically generalize equality testing, which is basically just discrimination of 2 elements. Discriminators allow partitioning and even sorting of arbitrary size lists in linear time without comparison operations (as in comparisonbased sorting) or arithmetization of the values (as for hashbased methods). Thus even references in ML could be discriminated in linear time instead of quadratic time, if discrimination were the builtin operation for exposing reference equality, not equality testing.