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The influence of caches on the performance of sorting
 IN PROCEEDINGS OF THE SEVENTH ANNUAL ACMSIAM SYMPOSIUM ON DISCRETE ALGORITHMS
, 1997
"... We investigate the effect that caches have on the performance of sorting algorithms both experimentally and analytically. To address the performance problems that high cache miss penalties introduce we restructure mergesort, quicksort, and heapsort in order to improve their cache locality. For all t ..."
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Cited by 121 (4 self)
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We investigate the effect that caches have on the performance of sorting algorithms both experimentally and analytically. To address the performance problems that high cache miss penalties introduce we restructure mergesort, quicksort, and heapsort in order to improve their cache locality. For all three algorithms the improvementincache performance leads to a reduction in total execution time. We also investigate the performance of radix sort. Despite the extremely low instruction count incurred by this linear time sorting algorithm, its relatively poor cache performance results in worse overall performance than the e cient comparison based sorting algorithms. For each algorithm we provide an analysis that closely predicts the number of cache misses incurred by the algorithm.
A framework for speeding up priorityqueue operations
, 2004
"... Abstract. We introduce a framework for reducing the number of element comparisons performed in priorityqueue operations. In particular, we give a priority queue which guarantees the worstcase cost of O(1) per minimum finding and insertion, and the worstcase cost of O(log n) with at most log n + O ..."
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Cited by 8 (7 self)
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Abstract. We introduce a framework for reducing the number of element comparisons performed in priorityqueue operations. In particular, we give a priority queue which guarantees the worstcase cost of O(1) per minimum finding and insertion, and the worstcase cost of O(log n) with at most log n + O(1) element comparisons per minimum deletion and deletion, improving the bound of 2log n + O(1) on the number of element comparisons known for binomial queues. Here, n denotes the number of elements stored in the data structure prior to the operation in question, and log n equals max {1,log 2 n}. We also give a priority queue that provides, in addition to the abovementioned methods, the prioritydecrease (or decreasekey) method. This priority queue achieves the worstcase cost of O(1) per minimum finding, insertion, and priority decrease; and the worstcase cost of O(log n) with at most log n + O(log log n) element comparisons per minimum deletion and deletion. CR Classification. E.1 [Data Structures]: Lists, stacks, and queues; E.2 [Data
Lander,“The Influence of Caches on the Performance
 of Sorting”, Eight Annual ACM Symposium on Discrete Algorithms
, 1996
"... We investigate the effect that caches have on the performance of sorting algorithms both experimentally and analytically. To address the performance problems that high cache miss penalties introduce we restructure mergesort, quicksort, and heapsort in order to improve their cache locality. For all t ..."
Abstract

Cited by 2 (0 self)
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We investigate the effect that caches have on the performance of sorting algorithms both experimentally and analytically. To address the performance problems that high cache miss penalties introduce we restructure mergesort, quicksort, and heapsort in order to improve their cache locality. For all three algorithms the improvement in cache performance leads to a reduction in total execution time. We also investigate the performance of radix sort. Despite the extremely low instruction count incurred by this linear time sorting algorithm, its relatively poor cache performance results in worse overall performance than the efficient comparison based sorting algorithms. For each algorithm we provide an analysis that closely predicts the number of cache misses incurred by the algorithm. Q 1999 Academic Press 1.
3 is a More Promising Algorithmic Parameter Than 2
 Comput. Math. Appl
, 1998
"... In this paper we have observed and shown that ternary systems are more promising than the more traditional binary systems used in computers. In particular, ternary number system, heaps on ternary trees, and quicksort with 3 partitions do indicate some theoretical advantages over the more established ..."
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Cited by 1 (0 self)
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In this paper we have observed and shown that ternary systems are more promising than the more traditional binary systems used in computers. In particular, ternary number system, heaps on ternary trees, and quicksort with 3 partitions do indicate some theoretical advantages over the more established binary systems. The magic Napierian e plays the crucial role to establish the results. The experimental data, supporting the analysis, have also been presented. Keywords: Analysis of algorithms; Performance evaluation; Quicksort; Heaps; Divide and conquer technique 1 Introduction With the invention of computers, 2parametric algebra, number system and graphs among other systems started to flourish with accelerated speed. Boolean algebra got its important applications in computer technology, binary number system has occupied the core of computer arithmetic, and binary trees have become inseparable in Revised version of ref. no. CAM 2974. y Corresponding Author. mathematical analysis...
Experimental evaluation of local heaps
"... Abstract. In this paper we present a cacheaware realization of a priority queue, named a local heap, which is a slight modification of a standard binary heap. The new data structure is cache efficient, has a small computational overhead, and achieves a good worstcase performance with respect to th ..."
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Abstract. In this paper we present a cacheaware realization of a priority queue, named a local heap, which is a slight modification of a standard binary heap. The new data structure is cache efficient, has a small computational overhead, and achieves a good worstcase performance with respect to the number of element comparisons and the number of element moves. We show both theoretically and experimentally that the data structure is competitive with a standard binary heap, provided that the number of elements stored is not small. 1.
and
"... We introduce a framework for reducing the number of element comparisons performed in priorityqueue operations. In particular, we give a priority queue which guarantees the worstcase cost of O(1) per minimum finding and insertion, and the worstcase cost of O(log n) with at most log n + O(1) element ..."
Abstract
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We introduce a framework for reducing the number of element comparisons performed in priorityqueue operations. In particular, we give a priority queue which guarantees the worstcase cost of O(1) per minimum finding and insertion, and the worstcase cost of O(log n) with at most log n + O(1) element comparisons per minimum deletion and deletion, improving the bound of 2log n + O(1) known for binomial queues. Here, n denotes the number of elements stored in the data structure prior to the operation in question, and log n equals log 2 (max {2, n}). As an immediate application of the priority queue developed, we obtain a sorting algorithm that is optimally adaptive with respect to the inversion measure of disorder, and that sorts a sequence having n elements and I inversions with at most n log (I/n) + O(n) element comparisons.
Abstract Caches and Algorithms
, 1996
"... In presenting this dissertation in partial ful llment of the requirements for the Doctoral degree at the University ofWashington, I agree that the Library shall make its copies freely available for inspection. I further agree that extensive copying of this dissertation is allowable only for scholar ..."
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In presenting this dissertation in partial ful llment of the requirements for the Doctoral degree at the University ofWashington, I agree that the Library shall make its copies freely available for inspection. I further agree that extensive copying of this dissertation is allowable only for scholarly purposes, consistent with \fair use&quot; as prescribed in the U.S. Copyright Law. Requests for copying or reproduction of this dissertation may be referred to University Micro lms, 1490 Eisenhower Place,