Sequential and Parallel Searching Algorithms
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Parallel search or multithreaded search is a way to increase search speed by using additional processors. Parallel search algorithms are classified by their scalability (that means the behavior of the algorithm as the number of processors becomes large) and their speed up.
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![Sequential Search Algorithms
The search starts at the first record and
moves through each record until a match is
made, or not made.
Time complexity: O(n), ie. grows linearly as
the no. of items increases
int arraySize = arr.length;
for(int i = 0; i < arraySize; i++) {
if(arr[i] == key) {
return i;
}
}
return -1;](https://image.slidesharecdn.com/sequentialandparallelsearchingalgorithms-210513211757/85/Sequential-and-Parallel-Searching-Algorithms-3-320.jpg)
![Sequential Parallel Search Algorithm
Input:- An unsorted array S and
the search element y
Output:- The position of y
The unsorted array is first decomposed into
n/p subarrays.
Complexity: O(log(n+1)/log(p+1))
for k = 0 to n-2
if k is even then
for i = 0 to (n/2)-1 do in parallel
If A[2i] > A[2i+1] then
Exchange A[2i] ↔ A[2i+1]
else
for i = 0 to (n/2)-2 do in parallel
If A[2i+1] > A[2i+2] then
Exchange A[2i+1] ↔ A[2i+2]](https://image.slidesharecdn.com/sequentialandparallelsearchingalgorithms-210513211757/85/Sequential-and-Parallel-Searching-Algorithms-7-320.jpg)
![References
● http://nptel.ac.in/courses/106106112/Module5/Lecture%202/Lecture%202.pdf
[Searching on a random sequence in parallel]
● https://researchgate.net/publication/267739792_High_Performance_Pattern_
Matching_on_Heterogeneous_Platform [High Performance Pattern Matching on
Heterogeneous Platform]](https://image.slidesharecdn.com/sequentialandparallelsearchingalgorithms-210513211757/85/Sequential-and-Parallel-Searching-Algorithms-9-320.jpg)
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Summation analysis of simple loops
Consider the following simple loop from the previous class to find the index of the
smallest element in an array :
int a[n]; // array has n elements 0 .. n-1
...
smallind = 0; // guess the first index is the location of the smallest
i = 1;
while (i < n) {
if ( a[i] < a[smallind] )
smallind = i; // correct the guess if it's wrong
i = i + 1;
}
Analysis of the loop: version 1
This might be called synthetic analysis in that we first determine the running times of
the parts of the loop, and then use this analysis to determine the running time of
the who ...
Alternate Sort
Alternate SortIOSR Journals Sorting algorithms are the main concepts of the subject Data Structures and It’s Applications. These
algorithms are designed in arranging the data elements in the sorted order. If the data elements are arranged
in sorted order , then the searching is very easier. Some algorithms are comparison sort and some are noncomparison
sort. The choice of a algorithm is based on the efficiency of the algorithm. I have designed one
algorithm called as Alternate Sort. The main aspect is that different technique of comparisons is involed. I have
presented the algorithm , It’s working and the examples and finally my paper is consisting of the program
listing.
Lec 03 - Sorting.pptx
Lec 03 - Sorting.pptxDr. Shaukat Wasi Sorting algorithms like selection sort, insertion sort, bubble sort, merge sort, and quick sort are discussed, along with their time complexities, classifications as internal or external sorts, efficiency measures, and whether they maintain the relative order of elements with equal keys (stability). Key concepts around sorting like sorting passes, order, and stability are also introduced. Different sorting algorithms are then explained in more detail through pseudocode algorithms.
Sorting and Searching Techniques
Sorting and Searching TechniquesProf Ansari Searching
Information retrieval in the required format is the common activity in all computer applications. This involves searching, sorting and merging. Searching methods are designed to take advantage of the file organization and optimize the search for a particular record or establish its absence.
Sequential Search
The simplest technique for searching an unordered table for a particular record is to scan each entry in the table in a sequential manner until the desired record is found. This is the most natural way of searching. In this method, we simply go through a list or a file till the required record is found or end of list or file is encountered. Ordering of list is not important
An Experiment to Determine and Compare Practical Efficiency of Insertion Sort...
An Experiment to Determine and Compare Practical Efficiency of Insertion Sort...Tosin Amuda Sorting is a fundamental operation in computer science (many programs use it as an intermediate step), and as a result a large number of good sorting algorithms have been developed. Which algorithm is best for a given application depends on—among other factors—the number of items to be sorted, the extent to which the items are already somewhat sorted, possible restrictions on the item values, and the kind of storage device to be used: main memory, disks, or tapes.
There are three reasons to study sorting algorithms. First, sorting algorithms illustrate many creative approaches to problem solving, and these approaches can be applied to solve other problems. Second, sorting algorithms are good for practicing fundamental programming techniques using selection statements, loops, methods, and arrays. Third, sorting algorithms are excellent examples to demonstrate algorithm performance.
However, this paper attempt to compare the practical efficiency of three sorting algorithms – Insertion, Quick and mere Sort using empirical analysis. The result of the experiment shows that insertion sort is a quadratic time sorting algorithm and that it’s more applicable to subarray that is sufficiently small. The merge sort performs better with larger size of input as compared to insertion sort. Quicksort runs the most efficiently.
Data Structures 6
Data Structures 6Dr.Umadevi V The document discusses various sorting algorithms including exchange sorts like bubble sort and quicksort, selection sorts like straight selection sort, and tree sorts like heap sort. For each algorithm, it provides an overview of the approach, pseudocode, analysis of time complexity, and examples. Key algorithms covered are bubble sort (O(n2)), quicksort (average O(n log n)), selection sort (O(n2)), and heap sort (O(n log n)).
Ijcse13 05-01-048
Ijcse13 05-01-048vital vital The document proposes a novel sorting algorithm that has a time complexity of O(n) in the best case and O(n2) in the worst case. It compares three elements at a time and rearranges them, similar to bubble sort but moving a smaller element left as well as a larger element right. Experiments show it outperforms bubble sort for larger data sets, with a runtime of 4859.96 seconds for 50,000 elements compared to 10311.56 seconds for bubble sort. The algorithm is analyzed and shown to meet its proposed time complexities.
Ijcse13 05-01-048
Ijcse13 05-01-048vital vital The document proposes a novel sorting algorithm that has a time complexity of O(n) in the best case and O(n2) in the worst case. It compares three elements at a time and rearranges them, similar to bubble sort but moving a smaller element left as well as a larger element right. Experiments show it outperforms bubble sort for larger data sets, with a runtime of 4859.96 seconds for 50,000 elements compared to 10311.56 seconds for bubble sort. The algorithm is analyzed and shown to meet its proposed time complexities.
Insersion & Bubble Sort in Algoritm
Insersion & Bubble Sort in AlgoritmEhsan Ehrari while some elements unsorted:
Using linear search, find the location in the sorted portion where the 1st element of the unsorted portion should be inserted
while some elements unsorted:
Using linear search, find the location in the sorted portion where the 1st element of the unsorted portion should be inserted
while some elements unsorted:
Using linear search, find the location in the sorted portion where the 1st element of the unsorted portion should be inserted
while some elements unsorted:
Using linear search, find the location in the sorted portion where the 1st element of the unsorted portion should be inserted
while some elements unsorted:
Using linear search, find the location in the sorted portion where the 1st element of the unsorted portion should be inserted
while some elements unsorted:
Using linear search, find the location in the sorted portion where the 1st element of the unsorted portion should be inserted
while some elements unsorted:
Using linear search, find the location in the sorted portion where the 1st element of the unsorted portion should be inserted
while some elements unsorted:
Using linear search, find the location in the sorted portion where the 1st element of the unsorted portion should be inserted
while some elements unsorted:
Using linear search, find the location in the sorted portion where the 1st element of the unsorted portion should be inserted
while some elements unsorted:
Using linear search, find the location in the sorted portion where the 1st element of the unsorted portion should be inserted
while some elements unsorted:
Using linear search, find the location in the sorted portion where the 1st element of the unsorted portion should be inserted
while some elements unsorted:
Using linear search, find the location in the sorted portion where the 1st element of the unsorted portion should be inserted
while some elements unsorted:
Using linear search, find the location in the sorted portion where the 1st element of the unsorted portion should be inserted
while some elements unsorted:
Using linear search, find the location in the sorted portion where the 1st element of the unsorted portion should be inserted
while some elements unsorted:
Using linear search, find the location in the sorted portion where the 1st element of the unsorted portion should be inserted
while some elements unsorted:
Using linear search, find the location in the sorted portion where the 1st element of the unsorted portion should be inserted
while some elements unsorted:
Using linear search, find the location in the sorted portion where the 1st element of the unsorted portion should be inserted
while some elements unsorted:
Using linear search, find the location in the sorted portion where the 1st element of the unsorted portion should be inserted
while some elements unsorted:
Using linear search, find the location in the sorted portion where the 1st element of the unsorted portion
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- 2. Introduction ● Parallel search or multithreaded search is a way to increase search speed by using additional processors. ● Parallel search algorithms are classified by their scalability (that means the behavior of the algorithm as the number of processors become large) and their speed up. ● The speed up is defined as the ratio of the running time of the sequential execution to the running time of the parallel execution. ● Speed up acts as an indicator for a parallel program’s performance.
- 3. Sequential Search Algorithms The search starts at the first record and moves through each record until a match is made, or not made. Time complexity: O(n), ie. grows linearly as the no. of items increases int arraySize = arr.length; for(int i = 0; i < arraySize; i++) { if(arr[i] == key) { return i; } } return -1;
- 4. Sequential vs. Parallel Searching
- 5. Example: Sequential Bubble Sort ● Compares two consecutive values at a time and swaps them if they are out of order. ● Greater values are being moved towards the end of the list. ● Number of comparisons and swaps: which corresponds to a time complexity O(n2 )
- 6. Example: Sequential Bubble Sort
- 7. Sequential Parallel Search Algorithm Input:- An unsorted array S and the search element y Output:- The position of y The unsorted array is first decomposed into n/p subarrays. Complexity: O(log(n+1)/log(p+1)) for k = 0 to n-2 if k is even then for i = 0 to (n/2)-1 do in parallel If A[2i] > A[2i+1] then Exchange A[2i] ↔ A[2i+1] else for i = 0 to (n/2)-2 do in parallel If A[2i+1] > A[2i+2] then Exchange A[2i+1] ↔ A[2i+2]
- 8. Example: Parallel Bubble Sort The basic idea is to run multiple iterations in parallel. Operates in two alternate phases: Phase-even: Even processes exchange values with right neighbors. Phase-odd: Odd processes exchange values with right neighbors.
- 9. References ● http://nptel.ac.in/courses/106106112/Module5/Lecture%202/Lecture%202.pdf [Searching on a random sequence in parallel] ● https://researchgate.net/publication/267739792_High_Performance_Pattern_ Matching_on_Heterogeneous_Platform [High Performance Pattern Matching on Heterogeneous Platform]