This paper introduces a sorting method (a block sort embedded with another sorting algorithm) to sort a large volume of records in external storage by means of a 2-component numeric key consisting of key 1 and key 2. ...
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This paper introduces a sorting method (a block sort embedded with another sorting algorithm) to sort a large volume of records in external storage by means of a 2-component numeric key consisting of key 1 and key 2. Based on the value of key 1, the data file is split into many subfiles. Each subfile contains records of common key 1, but with different key 2s. After sorting, all records are linked together by pointers to form a sort file. An index array keeps the list heads to these separate subfiles. The list structure in the file organization facilities updating of the file. This sorting method can be applied to a key of any number of digits. A subroutine of the methodology is illustrated in the Appendix.
We describe two simple optimal-work parallel algorithms for sorting a list L = (X(1),X(2),...,X(m)) of m strings over an arbitrary alphabet Sigma, where Sigma(i=1)(m) \X(i)\ = n and two elements of Sigma can be compar...
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We describe two simple optimal-work parallel algorithms for sorting a list L = (X(1),X(2),...,X(m)) of m strings over an arbitrary alphabet Sigma, where Sigma(i=1)(m) \X(i)\ = n and two elements of Sigma can be compared in unit time using a single processor. The first algorithm is a deterministic algorithm that runs in O(log(2) m/log log m) time and the second is a randomized algorithm that runs in O(log m) time. Both algorithms use O(mlog m + n) operations. Compared to the best-known parallel algorithms for sorting strings, our algorithms offer the following improvements. 1. The total number of operations used by our algorithms is optimal while all previous parallel algorithms use a nonoptimal number of operations. 2. We make no assumption about the alphabet while the previous algorithms assume that the alphabet is restricted to {1,2,...,n(0)(1)}. 3. The computation model assumed by our algorithms is the Common CRCW PRAM unlike the known algorithms that assume the Arbitrary CRCW PRAM. 4. Our algorithms use O(mlogm + n) space, while the previous parallel algorithms use O(n(1+epsilon)) space, where epsilon is a positive constant. We also present optimal-work parallel algorithms to construct a digital search tree for a given set of strings and to search for a string in a sorted list of strings. We use our parallel sorting algorithms to solve the problem of determining a minimal starting point of a circular string with respect to lexicographic ordering, Our solution improves upon the previous best-known result to solve this problem.
We propose new lossless medical image compression method based on hierarchical sorting technique. Hierarchical sorting is a technique to achieve high compression ratio by detecting the regions where image pattern vari...
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We propose new lossless medical image compression method based on hierarchical sorting technique. Hierarchical sorting is a technique to achieve high compression ratio by detecting the regions where image pattern varies abruptly and sorting pixel order by its value to increase predictability. In this method, we can control sorting accuracy along with size and complexity. As the result, we can reduce the sizes of the permutation-tables and rouse the tables to other image regions. Comparison using experimental implementation of this method shows better performance for medical image set measured by X-ray CT and MRI instruments where similar sub-block patterns appear frequently. This technique applies quad-tree division method to divide an image to blocks in order to support progressive decoding and fast preview of large images.
What is a sorting function-not a sorting function for a given ordering relation, but a sorting function with nothing given? Formulating four basic properties of sorting algorithms as defining requirements, we arrive a...
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What is a sorting function-not a sorting function for a given ordering relation, but a sorting function with nothing given? Formulating four basic properties of sorting algorithms as defining requirements, we arrive at intrinsic notions of sorting and stable sorting: a function is a sorting function if and only it is an intrinsically parametric permutation function. It is a stable sorting function if and only if it is an intrinsically stable permutation function. We show that ordering relations can be represented isomorphically as inequality tests, comparators and stable sorting functions, each with their own intrinsic characterizations, which in turn provide a basis for run-time monitoring of their expected I/O behaviors. The isomorphisms are parametrically polymorphically definable, which shows that it is sufficient to provide any one of the representations since the others are derivable without compromising data abstraction. Finally we point out that stable sorting functions as default representations of ordering relations have the advantage of permitting linear-time sorting algorithms;inequality tests forfeit this possibility. (C) 2008 Elsevier Inc. All rights reserved.
Let (W, S) be an arbitrary Coxeter system. For each word omega in the generators we define a partial order-called the omega-sorting order-on the set of group elements W-omega subset of W that occur as subwords of omeg...
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Let (W, S) be an arbitrary Coxeter system. For each word omega in the generators we define a partial order-called the omega-sorting order-on the set of group elements W-omega subset of W that occur as subwords of omega. We show that the omega-sorting order is a supersolvable join-distributive lattice and that it is strictly between the weak and Bruhat orders on the group. Moreover, the omega-sorting order is a "maximal lattice" in the sense that the addition of any collection of Bruhat covers results in a nonlattice. Along the way we define a class of structures called supersolvable antimatroids and we show that these are equivalent to the class of supersolvable join-distributive lattices. (C) 2009 Elsevier Inc. All rights reserved.
This study was a qualitative analysis of introductory-level computer science students' comprehension of sorting algorithms. We asked six students to read and summarize the code of a sorting algorithm. Next, we ask...
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ISBN:
(纸本)9781450358903
This study was a qualitative analysis of introductory-level computer science students' comprehension of sorting algorithms. We asked six students to read and summarize the code of a sorting algorithm. Next, we asked them to trace through the code, first using a provided array with pencil and paper, then again using sticky-notes to represent the array. Focused analysis of one interview found that a student who struggled to comprehend the algorithm during the pencil-and-paper trace quickly gained understanding when physically tracing the sticky-note array. We hypothesize that physical interaction with a sticky-note representation of an array facilitated student comprehension of sorting algorithms by consolidating multiple lines of code (LOC) into a single repeatable action. This research is relevant to computer science educators who may teach sorting algorithms to introductory-level students.
We present a cost optimal parallel algorithm for sorting presorted sequences. The measure of presortedness we consider is Rem, i.e., the smallest number of elements whose removal leaves a sorted sequence. The algorith...
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We present a cost optimal parallel algorithm for sorting presorted sequences. The measure of presortedness we consider is Rem, i.e., the smallest number of elements whose removal leaves a sorted sequence. The algorithm sorts a sequence X of length n, with Rem(X) = r, in O(log n) time, provided O(n + r log r)/log n) processors are available, in the EREW PRAM model. This is the first PRAM sorting algorithm which is cost optimal with respect to Rem.
We study the space requirements of a sorting algorithm where only items that at the end will be adjacent are kept together. This is equivalent to the following combinatorial problem: Consider a string of fixed length ...
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We study the space requirements of a sorting algorithm where only items that at the end will be adjacent are kept together. This is equivalent to the following combinatorial problem: Consider a string of fixed length n that starts as a string of 0's, and then evolves by changing each 0 to 1, with the n changes done in random order. What is the maximal number of runs of 1's? We give asymptotic results for the distribution and mean. It turns out that, as in many problems involving a maximum, the maximum is asymptotically normal, with fluctuations of order n (1/2), and to the first order well approximated by the number of runs at the instance when the expectation is maximized, in this case when half the elements have changed to 1;there is also a second order term of order n (1/3). We also treat some variations, including priority queues and sock-sorting. The proofs use methods originally developed for random graphs.
In this paper, we present a new parallel sorting algorithm which maximizes the overlap between the disk, network, and CPU subsystems of a processing node. This algorithm is shown to be of similar complexity to known e...
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In this paper, we present a new parallel sorting algorithm which maximizes the overlap between the disk, network, and CPU subsystems of a processing node. This algorithm is shown to be of similar complexity to known efficient sorting algorithms. The pipelining effect exploited by our algorithm should lead to higher levels of performance on distributed memory parallel processors. In order to achieve the best results using this strategy, the CPU, network, and disk operations must take comparable time. We suggest acceptable levels of system balance for sorting machines and analyze the performance of the sorting algorithm as system parameters vary. (C) 1995 Academic Press, Inc.
Modern nonvolatile memories (NVMs) are widely recognized as energy-efficient replacements of classical memory/storage media, such as SRAM, DRAM, and mechanical hard disk. Among the popular NVMs, the skyrmion racetrack...
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Modern nonvolatile memories (NVMs) are widely recognized as energy-efficient replacements of classical memory/storage media, such as SRAM, DRAM, and mechanical hard disk. Among the popular NVMs, the skyrmion racetrack memory (SK-RM) is well known for its high storage density and unique supports of insert/delete operations. However, the existing algorithms designed for classical media might experience serious performance degradation when working on the SK-RM, due to the distinct characteristics of SK-RM. Thus, the existing algorithms should be redesigned to adapt to the brand-new memory model based on the SK-RM, so as to fully reveal the potentials of SK-RM. In particular, many existing algorithms tend to access the in-memory data in a random-hopping fashion, which generates many time-consuming shift operations of SK-RM. It is therefore crucial for the existing algorithms to eliminate unnecessary shift operations of SK-RM to boost the performance of the algorithms. In many modern applications, such as multimedia and data analysis, it is a common operation to process two or more arrays/vectors of data to perform certain computation tasks. In the arrays/vectors, an appropriate data placement strategy is critical for avoiding unnecessary shift operations of SK-RM. The observation thus motivates this work in proposing a recursive back-to-back data placement manner to effectively reduces the shift operations of SK-RM. To demonstrate the back-to-back data placement, we take sorting algorithms as a case study, and propose a novel shift-limited sorting algorithm for SK-RM. Analytical studies show that the shift-limited sort effectively enhances the time complexity of classical merge sort from O(dn lg n) to O(n lg n), where d is the bit distance between adjacent access ports on the nanotracks of the SK-RM. After that, the efficacy of the proposed shift-limited sort is then verified by experimental studies, where the results are encouraging.
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