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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)
![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-210513211757/75/Sequential-and-Parallel-Searching-Algorithms-3-2048.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-210513211757/75/Sequential-and-Parallel-Searching-Algorithms-7-2048.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-210513211757/75/Sequential-and-Parallel-Searching-Algorithms-9-2048.jpg)
Uploaded bySuresh Pokharel
Sequential and Parallel Searching Algorithms
The document discusses sequential and parallel searching algorithms, highlighting the advantages of parallel search in enhancing speed through multi-threading. It outlines the time complexities of sequential search (O(n)) and parallel search (O(log(n+1)/log(p+1))), with examples like bubble sort to illustrate sequential and parallel processes. It also mentions the importance of scalability and speedup as performance indicators for parallel algorithms.
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Sequential and Parallel Searching Algorithms
- 1. Sequential and ParallelSearching Algorithms Presented By Nishant Mahat 074MSCSK006 Rachana Kunwar 074MSCSK009 Suresh Mainali 074MSCSK014 Suresh Pokharel 074MSCSK015 (Msc. In Computer System and Knowledge Engineering, Pulchowk Campus )
- 2. Introduction ● Parallel searchor 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 Thesearch 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. ParallelSearching
- 5. Example: Sequential BubbleSort ● 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.
- 7. Sequential Parallel SearchAlgorithm 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 BubbleSort 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 ona random sequence in parallel] ● https://researchgate.net/publication/267739792_High_Performance_Pattern_ Matching_on_Heterogeneous_Platform [High Performance Pattern Matching on Heterogeneous Platform]