A Behavioural Theory for Reflective Sequential Algorithms

2018 ◽  
pp. 117-131 ◽  
Author(s):  
Flavio Ferrarotti ◽  
Klaus-Dieter Schewe ◽  
Loredana Tec
Keyword(s):  
Sequential Algorithms ◽  
Behavioural Theory

scholarly journals A Behavioural Theory of Recursive Algorithms

2020 ◽  
Vol 177 (1) ◽  
pp. 1-37
Author(s):  
Egon Börger ◽  
Klaus-Dieter Schewe
Keyword(s):  
Recursive Algorithm ◽  
Recursive Algorithms ◽  
State Machines ◽  
Abstract State Machines ◽  
Sequential Algorithms ◽  
Machine Model ◽  
Behavioural Theory ◽  
Concurrent Algorithms ◽  
Definition Of

“What is an algorithm?” is a fundamental question of computer science. Gurevich’s behavioural theory of sequential algorithms (aka the sequential ASM thesis) gives a partial answer by defining (non-deterministic) sequential algorithms axiomatically, without referring to a particular machine model or programming language, and showing that they are captured by (nondeterministic) sequential Abstract State Machines (nd-seq ASMs). However, recursive algorithms such as mergesort are not covered by this theory, as has been pointed out by Moschovakis, who had independently developed a different framework to mathematically characterize the concept of (in particular recursive) algorithm. In this article we propose an axiomatic definition of the notion of sequential recursive algorithm which extends Gurevich’s axioms for sequential algorithms by a Recursion Postulate and allows us to prove that sequential recursive algorithms are captured by recursive Abstract State Machines, an extension of nd-seq ASMs by a CALL rule. Applying this recursive ASM thesis yields a characterization of sequential recursive algorithms as finitely composed concurrent algorithms all of whose concurrent runs are partial-order runs.

scholarly journals Practical Wavelet Tree Construction

2021 ◽  
Vol 26 ◽  
pp. 1-67
Author(s):  
Patrick Dinklage ◽  
Jonas Ellert ◽  
Johannes Fischer ◽  
Florian Kurpicz ◽  
Marvin Löbel
Keyword(s):  
Parallel Algorithms ◽  
Shared Memory ◽  
Distributed Memory ◽  
Auxiliary Information ◽  
Parallel Computers ◽  
External Memory ◽  
Sequential Algorithms ◽  
Bottom Up ◽  
Memory Efficiency ◽  
Tree Construction

We present new sequential and parallel algorithms for wavelet tree construction based on a new bottom-up technique. This technique makes use of the structure of the wavelet trees—refining the characters represented in a node of the tree with increasing depth—in an opposite way, by first computing the leaves (most refined), and then propagating this information upwards to the root of the tree. We first describe new sequential algorithms, both in RAM and external memory. Based on these results, we adapt these algorithms to parallel computers, where we address both shared memory and distributed memory settings. In practice, all our algorithms outperform previous ones in both time and memory efficiency, because we can compute all auxiliary information solely based on the information we obtained from computing the leaves. Most of our algorithms are also adapted to the wavelet matrix , a variant that is particularly suited for large alphabets.

Analisis Kinerja Algoritma Quick Double Merge Sort Paralel Menggunakan openMP

2020 ◽  
Vol 11 (2) ◽  
pp. 95-102
Author(s):  
I Nyoman Aditya Yudiswara ◽  
Abba Suganda
Keyword(s):  
Parallel Algorithms ◽  
The Other ◽  
Sorting Algorithm ◽  
Data Parallelism ◽  
Parallel Sorting ◽  
Sequential Algorithms ◽  
Sorting Algorithms ◽  
Merge Sort ◽  
Sort Algorithm ◽  
Quick Sort

Processor technology currently tends to increase the number of cores more than increasing the clock speed. This development is very useful and becomes an opportunity to improve the performance of sequential algorithms that are only done by one core. This paper discusses the sorting algorithm that is executed in parallel by several logical CPUs or cores using the openMP library. This algorithm is named QDM Sort which is a combination of sequential quick sort algorithm and double merge algorithm. This study uses a data parallelism approach to design parallel algorithms from sequential algorithms. The data used in this study are the data that have not been sorted and also the data that has been sorted is integer type which is stored in advance in a file. The parameter measured to determine the performance of the QDM Sort algorithm is speedup. In a condition where a large amount of data is above 4096 and the number of threads in QDM Sort is the same as the number of logical CPUs, the QDM Sort algorithm has a better speedup compared to the other parallel sorting algorithms discussed in this study. For small amounts of data it is still better to use sequential sorting algorithm.

Behavioural Foundations of DBT

2017 ◽  
pp. 68-90
Author(s):  
Alexander L. Chapman
Keyword(s):  
Evidence Based ◽  
Case Formulation ◽  
Behaviour Therapy ◽  
Dialectical Behaviour Therapy ◽  
Behavioural Theory ◽  
Evaluation Of Evidence ◽  
Ongoing Assessment

This chapter discusses and illustrates how behavioural theory and science guide many aspects of Dialectical Behaviour Therapy (DBT). Behavioural theory informs the DBT approach to case formulation, the ongoing assessment of behaviour throughout therapy, and the selection, implementation, and evaluation of evidence-based interventions. As described later in this chapter, behavioural theory also influences the style and “flavour” of DBT sessions. As the focus of the first stage of DBT often focuses on the treatment of suicidal and self-injurious behaviour, the chapter emphasizes these behaviours via several examples, and also touches upon some other common behaviours targeted in DBT.

scholarly journals Behavioural Theory at Work: Program Transformations in a Service-Centred Calculus

2008 ◽  
pp. 59-77 ◽  
Author(s):  
Luís Cruz-Filipe ◽  
Ivan Lanese ◽  
Francisco Martins ◽  
António Ravara ◽  
Vasco T. Vasconcelos
Keyword(s):  
Program Transformations ◽  
Work Program ◽  
Behavioural Theory

scholarly journals AN OPTIMAL PARALLEL ALGORITHM FOR DETECTING WEAK VISIBILITY OF A SIMPLE POLYGON

1995 ◽  
Vol 05 (01n02) ◽  
pp. 93-124 ◽  
Author(s):  
DANNY Z. CHEN
Keyword(s):  
Computational Model ◽  
Parallel Algorithm ◽  
Simple Polygon ◽  
Sequential Algorithms ◽  
Crew Pram

The problem of detecting the weak visibility of an n-vertex simple polygon P is that of finding whether P is weakly visible from one of its edges and (if it is) identifying every edge from which P is weakly visible. In this paper, we present an optimal parallel algorithm for solving this problem. Our algorithm runs in O(log n) time using O(n/log n) processors in the CREW PRAM computational model, and is very different from the sequential algorithms for this problem. Based on this algorithm, several other problems related to weak visibility can be optimally solved in parallel.

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