Enhanced Matrix Chain Multiplication

Suvarna, B.; Padmaja, T. Maruthi

doi:10.13052/jcsm2245-1439.743

Cited by 3 publications

(3 citation statements)

References 4 publications

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“…Follow the same procedure for all remaining elements in the array until the elements are sorted [1]. Applications of Selection Sort are, it is used for small data sets and can be used in Embedded System [11].…”

Section: Min_ Min_ + = Positiion Positionmentioning

confidence: 99%

“…Sorting algorithms are widely used in different applications like finding duplicates from a set of elements, Ranking i.e. for a given set of elements finding the minimum and maximum, median, numerical applications, Matrix chain Multiplication [1], Kruskal algorithm and in searching. Searching can be done easily after performing the sorting.…”

Section: Introductionmentioning

confidence: 99%

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Performance Comparison of Sorting Algorithms with Random Numbers as Inputs

Buradagunta¹,

Bodapati²,

Mundukur³

et al. 2020

ISI

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Sorting is a huge demand research area in computer science. Sorting is a process of arranging the elements in an order. In practical application computing requires things to be in order. A comparative study is done in this study and performed the comparison for both positive and negative numbers by taking the random numbers as input. In this study, different algorithms like UNH Sort, Selection Sort, Bubble Sort, Insertion Sort, Merge Sort, Quick Sort are considered for the experimentation. From the obtained results we can conclude that, Initially when the input size is less UNH Sort is giving best when it is compared with bubble sort. When the input size increases bubble sort takes long time to perform sorting. And Quick sort takes less time to perform sorting.

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Section: Min_ Min_ + = Positiion Positionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance Comparison of Sorting Algorithms with Random Numbers as Inputs

Buradagunta¹,

Bodapati²,

Mundukur³

et al. 2020

ISI

View full text Add to dashboard Cite

show abstract

“…In this series, parallelization of can be solved using DP approach with time and space complexity O(n 3 ) and O(n 2 ), respectivelly. However, various heuristics are also experimented to reduce the time complexity for this category of computation [33].…”

Section: MCMmentioning

confidence: 99%

A Parallelization of Non-Serial Polyadic Dynamic Programming on GPU

Diwan

Tembhurne

2019

CIT. J. comput. inf. technol

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Parallelization of Non-Serial Polyadic Dynamic Programming (NPDP) on high-throughput manycore architectures, such as NVIDIA GPUs, suffers from load imbalance, i.e. non-optimal mapping between the sub-problems of NPDP and the processing elements of the GPU. NPDP exhibits non-uniformity in the number of subproblems as well as computational complexity across the phases. In NPDP parallelization, phases are computed sequentially whereas subproblems of each phase are computed concurrently. Therefore, it is essential to effectively map the subproblems of each phase to the processing elements while implementing thread level parallelism. We propose an adaptive Generalized Mapping Method (GMM) for NPDP parallelization that utilizes the GPU for efficient mapping of subproblems onto processing threads in each phase. Input-size and targeted GPU decide the computing power and the best mapping for each phase in NPDP parallelization. The performance of GMM is compared with different conventional parallelization approaches. For sufficiently large inputs, our technique outperforms the state-of-the-art conventional parallelization approach and achieves a significant speedup of a factor 30. We also summarize the general heuristics for achieving better gain in the NPDP parallelization.

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How to Learn Dynamic Programming from Scratch?

Malik

Ali

et al. 2021

2021 Innovation and New Trends in Engineering, Science and Technology Education Conference (IETSEC)

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Enhanced Matrix Chain Multiplication

Cited by 3 publications

References 4 publications

Performance Comparison of Sorting Algorithms with Random Numbers as Inputs

Performance Comparison of Sorting Algorithms with Random Numbers as Inputs

A Parallelization of Non-Serial Polyadic Dynamic Programming on GPU

How to Learn Dynamic Programming from Scratch?

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