Performance evaluation of parallel bubble sort algorithm on supercomputer IMAN1.
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The document presents a paper on the performance evaluation of a parallel bubble sort algorithm using the supercomputer Iman1, highlighting its running time, speedup, and efficiency as the number of processors increases. It discusses various background studies and objectives, particularly emphasizing the application's benefits for supercomputers in handling large data sets with improved efficiency and reduced cost. The parallel bubble sort demonstrates a complexity of O(n), outperforming the traditional O(n log n) complexity of sequential algorithms.
Performance evaluation of parallel bubble sort algorithm on supercomputer IMAN1.
- 1. A PAPER BASED PRESENTATION – “PERFORMANCE EVALUATION OF PARALLEL BUBBLE SORT ALGORITHM ON SUPER COMPUTER IMAN1.”
- 2. Introductory Prepared By Shakil Ahmed 19-91472-3 Nazia Alfaz 19-91493-3 Submitted To Dr. Ashraf Uddin Assistant Professor Department of Computer Science American International University - Bangladesh
- 3. Course Outline Lesson 1. Overview Of The Paper. Lesson 2. Background Study. Lesson 3. Objective of this Paper. Lesson 4. Usefulness/Applications Lesson 5. Algorithm Used in this Paper.
- 4. Overview • This paper published the running time, parallel speedup and parallel efficiency of parallel bubble sort. • The parallel bubble sort is evaluated in Message Passing Interface (MPI) and measured in supercomputer IMAN1. • The parallel bubble sort has the better running time as the number of processors increases. • Over small number of processors parallel bubble sort is also more efficient.
- 5. Background Study ◦ Mm. Jiang and D. Crookes. (2006), proposed high-sample rate 3-Dimention (3D) median filtering processor architecture that can reduce the computation complexity in comparison with the traditional bubble sorting algorithm. ◦ Kale V, Solomonik E. (2010), proposed a method for common patterns of parallel sorting algorithms in terms of load balancing, keys distribution, communication cost, and computations cost. ◦ A. I. Elnashar. (2011), used a Massage Passing Interface Chameleon (MPICH2) in a dual-core processor machine which had the windows operating system in order to explore how the number of cores and the processes affect the performance of a parallel bubble sort algorithm. ◦ N. Sismanis, N. Pitsianis, and X. Sun, (2012), had done a study of parallel bubble sort algorithm for K- Nearest Neighbor (KNN) search in introduces. The study is conducted on a parallel processor or accelerator that favors synchronous operations and has high synchronization cost. ◦ O. AbuAlghanam, M. Qatawneh, H.al Ofeishat, O. adwan, A. Huneiti. (2017), several mechanisms for secured message passing use bubble sort algorithm to tackle security problems of information by enhancing the process of encryption and decryption the text message.
- 6. Objectives • Run Time Evaluation: As the number of processors increase, the run time is reduced due to parallelism (2 to 32 processors).
- 7. Objectives ◦ Speedup Evaluation: Parallel speedup increases when the number of processors increases. The parallel algorithm achieves the best speedup on a large number of processors. It is the ratio of sequential bubble sort time and parallel bubble sort time.
- 8. Objectives ◦ Parallel Efficiency: Parallel efficiency is the ratio between speedup and the number of processors.
- 9. Objectives ◦ Parallel Cost: The product of parallel run time and the number of processors.
- 10. Application & Usefulness 1. The parallel bubble sort is mainly useful for the supercomputers that works with too many processors. Supercomputers have been used primarily for scientific and engineering work requiring exceedingly high-speed computations. By using parallel bubble sort algorithm the processors can work in parallel. 2. As the number of processors increases the run time is reduced due to better parallelism and better load distribution among more processors. 3. The parallel algorithm achieves the best speedup on a large number of processors. 4. When the number of processors is equal to two, the parallel bubble sort achieves about 26% parallel efficiency. So for small number of processor the algorithm has high efficiency. 5. When parallel bubble sort algorithm works with 2,4 or 8 processor the cost is reduced. So if we want to work with smaller number of processor in a cost effective way, the algorithm is useful. 6. The complexity of parallel bubble sort equals to O(n) which is much better than the complexity of the fastest known sequential sorting algorithm with O(n log n) complexity
- 11. Algorithm #initialization of input n For i in 1 to n do If i is odd then For j in 0 to n/2-1 do Compare input (2 * j+1) with input (2 * j+2); If input(2 * j+1)> input (2 * j+2) then swap; end for; If i is even then For j in 1 to n/2-1 do Compare input (2 * j) with input (2 * j+1); If input(2 * j)> input (2 * j+1) then swap; end for; end for
- 12. Explanation ◦ 1.The parallel version of bubble sort will perform n iterations of the main loop and for each iteration. ◦ 2. For each iteration, n-1 comparisons and (n-1)/2 exchanges ◦ 3. There are two loops, the outer loop will take n steps and the inner loop depends on the odd or even phase which takes n/2 steps and that time will be divided by the number of processors. So the time complexity that is required for all processors to sort the dataset is O (n2 /2p). ◦ 4. When the number of processor is greater that n/2, The complexity of the inner loop will be O(1) because all the iteration will be performed in parallel and the complexity of the algorithm will be O(n).
- 13. THANK YOU!