Soumettre la recherche
Mettre en ligne
An Optimized Parallel Algorithm for Longest Common Subsequence Using Openmp – A Review
•
0 j'aime
•
86 vues
IRJET Journal
Suivre
https://irjet.net/archives/V3/i2/IRJET-V3I2205.pdf
Lire moins
Lire la suite
Ingénierie
Signaler
Partager
Signaler
Partager
1 sur 4
Télécharger maintenant
Télécharger pour lire hors ligne
Recommandé
USING LEARNING AUTOMATA AND GENETIC ALGORITHMS TO IMPROVE THE QUALITY OF SERV...
USING LEARNING AUTOMATA AND GENETIC ALGORITHMS TO IMPROVE THE QUALITY OF SERV...
IJCSEA Journal
Ak04605259264
Ak04605259264
IJERA Editor
LATTICE BASED TOOLS IN CRYPTANALYSIS FOR PUBLIC KEY CRYPTOGRAPHY
LATTICE BASED TOOLS IN CRYPTANALYSIS FOR PUBLIC KEY CRYPTOGRAPHY
IJNSA Journal
20 26
20 26
Ijarcsee Journal
HyperLogLog in Practice: Algorithmic Engineering of a State of The Art Cardin...
HyperLogLog in Practice: Algorithmic Engineering of a State of The Art Cardin...
Sunny Kr
A HYBRID CLUSTERING ALGORITHM FOR DATA MINING
A HYBRID CLUSTERING ALGORITHM FOR DATA MINING
cscpconf
Fk3110791084
Fk3110791084
IJERA Editor
Av33274282
Av33274282
IJERA Editor
Recommandé
USING LEARNING AUTOMATA AND GENETIC ALGORITHMS TO IMPROVE THE QUALITY OF SERV...
USING LEARNING AUTOMATA AND GENETIC ALGORITHMS TO IMPROVE THE QUALITY OF SERV...
IJCSEA Journal
Ak04605259264
Ak04605259264
IJERA Editor
LATTICE BASED TOOLS IN CRYPTANALYSIS FOR PUBLIC KEY CRYPTOGRAPHY
LATTICE BASED TOOLS IN CRYPTANALYSIS FOR PUBLIC KEY CRYPTOGRAPHY
IJNSA Journal
20 26
20 26
Ijarcsee Journal
HyperLogLog in Practice: Algorithmic Engineering of a State of The Art Cardin...
HyperLogLog in Practice: Algorithmic Engineering of a State of The Art Cardin...
Sunny Kr
A HYBRID CLUSTERING ALGORITHM FOR DATA MINING
A HYBRID CLUSTERING ALGORITHM FOR DATA MINING
cscpconf
Fk3110791084
Fk3110791084
IJERA Editor
Av33274282
Av33274282
IJERA Editor
E01113138
E01113138
IOSR Journals
Parallel k nn on gpu architecture using opencl
Parallel k nn on gpu architecture using opencl
eSAT Publishing House
Analysis of Algorithms II - PS3
Analysis of Algorithms II - PS3
AtakanAral
A COMPARATIVE STUDY OF BACKPROPAGATION ALGORITHMS IN FINANCIAL PREDICTION
A COMPARATIVE STUDY OF BACKPROPAGATION ALGORITHMS IN FINANCIAL PREDICTION
IJCSEA Journal
Welcome to International Journal of Engineering Research and Development (IJERD)
Welcome to International Journal of Engineering Research and Development (IJERD)
IJERD Editor
IRJET- Wavelet Transform based Steganography
IRJET- Wavelet Transform based Steganography
IRJET Journal
Novel algorithms for Knowledge discovery from neural networks in Classificat...
Novel algorithms for Knowledge discovery from neural networks in Classificat...
Dr.(Mrs).Gethsiyal Augasta
Experimental study of Data clustering using k- Means and modified algorithms
Experimental study of Data clustering using k- Means and modified algorithms
IJDKP
An Efficient Frame Embedding Using Haar Wavelet Coefficients And Orthogonal C...
An Efficient Frame Embedding Using Haar Wavelet Coefficients And Orthogonal C...
IJERA Editor
Dynamic Texture Coding using Modified Haar Wavelet with CUDA
Dynamic Texture Coding using Modified Haar Wavelet with CUDA
IJERA Editor
Vol 16 No 2 - July-December 2016
Vol 16 No 2 - July-December 2016
ijcsbi
Extended pso algorithm for improvement problems k means clustering algorithm
Extended pso algorithm for improvement problems k means clustering algorithm
IJMIT JOURNAL
Simulation of Single and Multilayer of Artificial Neural Network using Verilog
Simulation of Single and Multilayer of Artificial Neural Network using Verilog
ijsrd.com
Combination of Immune Genetic Particle Swarm Optimization algorithm with BP a...
Combination of Immune Genetic Particle Swarm Optimization algorithm with BP a...
paperpublications3
Neural Network Algorithm for Radar Signal Recognition
Neural Network Algorithm for Radar Signal Recognition
IJERA Editor
SPEED-UP IMPROVEMENT USING PARALLEL APPROACH IN IMAGE STEGANOGRAPHY
SPEED-UP IMPROVEMENT USING PARALLEL APPROACH IN IMAGE STEGANOGRAPHY
csandit
Job Scheduling on the Grid Environment using Max-Min Firefly Algorithm
Job Scheduling on the Grid Environment using Max-Min Firefly Algorithm
Editor IJCATR
Kernal based speaker specific feature extraction and its applications in iTau...
Kernal based speaker specific feature extraction and its applications in iTau...
TELKOMNIKA JOURNAL
Gk3611601162
Gk3611601162
IJERA Editor
User_42751212015Module1and2pagestocompetework.pdf.docx
User_42751212015Module1and2pagestocompetework.pdf.docx
dickonsondorris
Problems in Task Scheduling in Multiprocessor System
Problems in Task Scheduling in Multiprocessor System
ijtsrd
cis97003
cis97003
perfj
Contenu connexe
Tendances
E01113138
E01113138
IOSR Journals
Parallel k nn on gpu architecture using opencl
Parallel k nn on gpu architecture using opencl
eSAT Publishing House
Analysis of Algorithms II - PS3
Analysis of Algorithms II - PS3
AtakanAral
A COMPARATIVE STUDY OF BACKPROPAGATION ALGORITHMS IN FINANCIAL PREDICTION
A COMPARATIVE STUDY OF BACKPROPAGATION ALGORITHMS IN FINANCIAL PREDICTION
IJCSEA Journal
Welcome to International Journal of Engineering Research and Development (IJERD)
Welcome to International Journal of Engineering Research and Development (IJERD)
IJERD Editor
IRJET- Wavelet Transform based Steganography
IRJET- Wavelet Transform based Steganography
IRJET Journal
Novel algorithms for Knowledge discovery from neural networks in Classificat...
Novel algorithms for Knowledge discovery from neural networks in Classificat...
Dr.(Mrs).Gethsiyal Augasta
Experimental study of Data clustering using k- Means and modified algorithms
Experimental study of Data clustering using k- Means and modified algorithms
IJDKP
An Efficient Frame Embedding Using Haar Wavelet Coefficients And Orthogonal C...
An Efficient Frame Embedding Using Haar Wavelet Coefficients And Orthogonal C...
IJERA Editor
Dynamic Texture Coding using Modified Haar Wavelet with CUDA
Dynamic Texture Coding using Modified Haar Wavelet with CUDA
IJERA Editor
Vol 16 No 2 - July-December 2016
Vol 16 No 2 - July-December 2016
ijcsbi
Extended pso algorithm for improvement problems k means clustering algorithm
Extended pso algorithm for improvement problems k means clustering algorithm
IJMIT JOURNAL
Simulation of Single and Multilayer of Artificial Neural Network using Verilog
Simulation of Single and Multilayer of Artificial Neural Network using Verilog
ijsrd.com
Combination of Immune Genetic Particle Swarm Optimization algorithm with BP a...
Combination of Immune Genetic Particle Swarm Optimization algorithm with BP a...
paperpublications3
Neural Network Algorithm for Radar Signal Recognition
Neural Network Algorithm for Radar Signal Recognition
IJERA Editor
SPEED-UP IMPROVEMENT USING PARALLEL APPROACH IN IMAGE STEGANOGRAPHY
SPEED-UP IMPROVEMENT USING PARALLEL APPROACH IN IMAGE STEGANOGRAPHY
csandit
Job Scheduling on the Grid Environment using Max-Min Firefly Algorithm
Job Scheduling on the Grid Environment using Max-Min Firefly Algorithm
Editor IJCATR
Tendances
(17)
E01113138
E01113138
Parallel k nn on gpu architecture using opencl
Parallel k nn on gpu architecture using opencl
Analysis of Algorithms II - PS3
Analysis of Algorithms II - PS3
A COMPARATIVE STUDY OF BACKPROPAGATION ALGORITHMS IN FINANCIAL PREDICTION
A COMPARATIVE STUDY OF BACKPROPAGATION ALGORITHMS IN FINANCIAL PREDICTION
Welcome to International Journal of Engineering Research and Development (IJERD)
Welcome to International Journal of Engineering Research and Development (IJERD)
IRJET- Wavelet Transform based Steganography
IRJET- Wavelet Transform based Steganography
Novel algorithms for Knowledge discovery from neural networks in Classificat...
Novel algorithms for Knowledge discovery from neural networks in Classificat...
Experimental study of Data clustering using k- Means and modified algorithms
Experimental study of Data clustering using k- Means and modified algorithms
An Efficient Frame Embedding Using Haar Wavelet Coefficients And Orthogonal C...
An Efficient Frame Embedding Using Haar Wavelet Coefficients And Orthogonal C...
Dynamic Texture Coding using Modified Haar Wavelet with CUDA
Dynamic Texture Coding using Modified Haar Wavelet with CUDA
Vol 16 No 2 - July-December 2016
Vol 16 No 2 - July-December 2016
Extended pso algorithm for improvement problems k means clustering algorithm
Extended pso algorithm for improvement problems k means clustering algorithm
Simulation of Single and Multilayer of Artificial Neural Network using Verilog
Simulation of Single and Multilayer of Artificial Neural Network using Verilog
Combination of Immune Genetic Particle Swarm Optimization algorithm with BP a...
Combination of Immune Genetic Particle Swarm Optimization algorithm with BP a...
Neural Network Algorithm for Radar Signal Recognition
Neural Network Algorithm for Radar Signal Recognition
SPEED-UP IMPROVEMENT USING PARALLEL APPROACH IN IMAGE STEGANOGRAPHY
SPEED-UP IMPROVEMENT USING PARALLEL APPROACH IN IMAGE STEGANOGRAPHY
Job Scheduling on the Grid Environment using Max-Min Firefly Algorithm
Job Scheduling on the Grid Environment using Max-Min Firefly Algorithm
Similaire à An Optimized Parallel Algorithm for Longest Common Subsequence Using Openmp – A Review
Kernal based speaker specific feature extraction and its applications in iTau...
Kernal based speaker specific feature extraction and its applications in iTau...
TELKOMNIKA JOURNAL
Gk3611601162
Gk3611601162
IJERA Editor
User_42751212015Module1and2pagestocompetework.pdf.docx
User_42751212015Module1and2pagestocompetework.pdf.docx
dickonsondorris
Problems in Task Scheduling in Multiprocessor System
Problems in Task Scheduling in Multiprocessor System
ijtsrd
cis97003
cis97003
perfj
Performance Comparision of Machine Learning Algorithms
Performance Comparision of Machine Learning Algorithms
Dinusha Dilanka
M017327378
M017327378
IOSR Journals
Scheduling Using Multi Objective Genetic Algorithm
Scheduling Using Multi Objective Genetic Algorithm
iosrjce
Parallelization of Graceful Labeling Using Open MP
Parallelization of Graceful Labeling Using Open MP
IJSRED
L010628894
L010628894
IOSR Journals
A Comparative study of K-SVD and WSQ Algorithms in Fingerprint Compression Te...
A Comparative study of K-SVD and WSQ Algorithms in Fingerprint Compression Te...
IRJET Journal
IJCER (www.ijceronline.com) International Journal of computational Engineerin...
IJCER (www.ijceronline.com) International Journal of computational Engineerin...
ijceronline
SVD BASED LATENT SEMANTIC INDEXING WITH USE OF THE GPU COMPUTATIONS
SVD BASED LATENT SEMANTIC INDEXING WITH USE OF THE GPU COMPUTATIONS
ijscmcj
Parallel Batch-Dynamic Graphs: Algorithms and Lower Bounds
Parallel Batch-Dynamic Graphs: Algorithms and Lower Bounds
Subhajit Sahu
Parallel Batch-Dynamic Graphs: Algorithms and Lower Bounds
Parallel Batch-Dynamic Graphs: Algorithms and Lower Bounds
Subhajit Sahu
BIDIRECTIONAL LONG SHORT-TERM MEMORY (BILSTM)WITH CONDITIONAL RANDOM FIELDS (...
BIDIRECTIONAL LONG SHORT-TERM MEMORY (BILSTM)WITH CONDITIONAL RANDOM FIELDS (...
kevig
BIDIRECTIONAL LONG SHORT-TERM MEMORY (BILSTM)WITH CONDITIONAL RANDOM FIELDS (...
BIDIRECTIONAL LONG SHORT-TERM MEMORY (BILSTM)WITH CONDITIONAL RANDOM FIELDS (...
ijnlc
An Algorithm For Vector Quantizer Design
An Algorithm For Vector Quantizer Design
Angie Miller
Information-Flow Analysis of Design Breaks up
Information-Flow Analysis of Design Breaks up
Eswar Publications
Scaling Application on High Performance Computing Clusters and Analysis of th...
Scaling Application on High Performance Computing Clusters and Analysis of th...
Rusif Eyvazli
Similaire à An Optimized Parallel Algorithm for Longest Common Subsequence Using Openmp – A Review
(20)
Kernal based speaker specific feature extraction and its applications in iTau...
Kernal based speaker specific feature extraction and its applications in iTau...
Gk3611601162
Gk3611601162
User_42751212015Module1and2pagestocompetework.pdf.docx
User_42751212015Module1and2pagestocompetework.pdf.docx
Problems in Task Scheduling in Multiprocessor System
Problems in Task Scheduling in Multiprocessor System
cis97003
cis97003
Performance Comparision of Machine Learning Algorithms
Performance Comparision of Machine Learning Algorithms
M017327378
M017327378
Scheduling Using Multi Objective Genetic Algorithm
Scheduling Using Multi Objective Genetic Algorithm
Parallelization of Graceful Labeling Using Open MP
Parallelization of Graceful Labeling Using Open MP
L010628894
L010628894
A Comparative study of K-SVD and WSQ Algorithms in Fingerprint Compression Te...
A Comparative study of K-SVD and WSQ Algorithms in Fingerprint Compression Te...
IJCER (www.ijceronline.com) International Journal of computational Engineerin...
IJCER (www.ijceronline.com) International Journal of computational Engineerin...
SVD BASED LATENT SEMANTIC INDEXING WITH USE OF THE GPU COMPUTATIONS
SVD BASED LATENT SEMANTIC INDEXING WITH USE OF THE GPU COMPUTATIONS
Parallel Batch-Dynamic Graphs: Algorithms and Lower Bounds
Parallel Batch-Dynamic Graphs: Algorithms and Lower Bounds
Parallel Batch-Dynamic Graphs: Algorithms and Lower Bounds
Parallel Batch-Dynamic Graphs: Algorithms and Lower Bounds
BIDIRECTIONAL LONG SHORT-TERM MEMORY (BILSTM)WITH CONDITIONAL RANDOM FIELDS (...
BIDIRECTIONAL LONG SHORT-TERM MEMORY (BILSTM)WITH CONDITIONAL RANDOM FIELDS (...
BIDIRECTIONAL LONG SHORT-TERM MEMORY (BILSTM)WITH CONDITIONAL RANDOM FIELDS (...
BIDIRECTIONAL LONG SHORT-TERM MEMORY (BILSTM)WITH CONDITIONAL RANDOM FIELDS (...
An Algorithm For Vector Quantizer Design
An Algorithm For Vector Quantizer Design
Information-Flow Analysis of Design Breaks up
Information-Flow Analysis of Design Breaks up
Scaling Application on High Performance Computing Clusters and Analysis of th...
Scaling Application on High Performance Computing Clusters and Analysis of th...
Plus de IRJET Journal
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...
IRJET Journal
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTURE
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTURE
IRJET Journal
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...
IRJET Journal
Effect of Camber and Angles of Attack on Airfoil Characteristics
Effect of Camber and Angles of Attack on Airfoil Characteristics
IRJET Journal
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...
IRJET Journal
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...
IRJET Journal
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...
IRJET Journal
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...
IRJET Journal
A REVIEW ON MACHINE LEARNING IN ADAS
A REVIEW ON MACHINE LEARNING IN ADAS
IRJET Journal
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...
IRJET Journal
P.E.B. Framed Structure Design and Analysis Using STAAD Pro
P.E.B. Framed Structure Design and Analysis Using STAAD Pro
IRJET Journal
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...
IRJET Journal
Survey Paper on Cloud-Based Secured Healthcare System
Survey Paper on Cloud-Based Secured Healthcare System
IRJET Journal
Review on studies and research on widening of existing concrete bridges
Review on studies and research on widening of existing concrete bridges
IRJET Journal
React based fullstack edtech web application
React based fullstack edtech web application
IRJET Journal
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...
IRJET Journal
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.
IRJET Journal
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...
IRJET Journal
Multistoried and Multi Bay Steel Building Frame by using Seismic Design
Multistoried and Multi Bay Steel Building Frame by using Seismic Design
IRJET Journal
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...
IRJET Journal
Plus de IRJET Journal
(20)
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTURE
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTURE
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...
Effect of Camber and Angles of Attack on Airfoil Characteristics
Effect of Camber and Angles of Attack on Airfoil Characteristics
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...
A REVIEW ON MACHINE LEARNING IN ADAS
A REVIEW ON MACHINE LEARNING IN ADAS
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...
P.E.B. Framed Structure Design and Analysis Using STAAD Pro
P.E.B. Framed Structure Design and Analysis Using STAAD Pro
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...
Survey Paper on Cloud-Based Secured Healthcare System
Survey Paper on Cloud-Based Secured Healthcare System
Review on studies and research on widening of existing concrete bridges
Review on studies and research on widening of existing concrete bridges
React based fullstack edtech web application
React based fullstack edtech web application
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...
Multistoried and Multi Bay Steel Building Frame by using Seismic Design
Multistoried and Multi Bay Steel Building Frame by using Seismic Design
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...
Dernier
NFPA 5000 2024 standard .
NFPA 5000 2024 standard .
DerechoLaboralIndivi
data_management_and _data_science_cheat_sheet.pdf
data_management_and _data_science_cheat_sheet.pdf
JiananWang21
Booking open Available Pune Call Girls Pargaon 6297143586 Call Hot Indian Gi...
Booking open Available Pune Call Girls Pargaon 6297143586 Call Hot Indian Gi...
Call Girls in Nagpur High Profile
CCS335 _ Neural Networks and Deep Learning Laboratory_Lab Complete Record
CCS335 _ Neural Networks and Deep Learning Laboratory_Lab Complete Record
Asst.prof M.Gokilavani
Call Girls Wakad Call Me 7737669865 Budget Friendly No Advance Booking
Call Girls Wakad Call Me 7737669865 Budget Friendly No Advance Booking
roncy bisnoi
VIP Model Call Girls Kothrud ( Pune ) Call ON 8005736733 Starting From 5K to ...
VIP Model Call Girls Kothrud ( Pune ) Call ON 8005736733 Starting From 5K to ...
SUHANI PANDEY
Unit 1 - Soil Classification and Compaction.pdf
Unit 1 - Soil Classification and Compaction.pdf
RagavanV2
VIP Call Girls Ankleshwar 7001035870 Whatsapp Number, 24/07 Booking
VIP Call Girls Ankleshwar 7001035870 Whatsapp Number, 24/07 Booking
dharasingh5698
ONLINE FOOD ORDER SYSTEM PROJECT REPORT.pdf
ONLINE FOOD ORDER SYSTEM PROJECT REPORT.pdf
Kamal Acharya
chapter 5.pptx: drainage and irrigation engineering
chapter 5.pptx: drainage and irrigation engineering
mulugeta48
AKTU Computer Networks notes --- Unit 3.pdf
AKTU Computer Networks notes --- Unit 3.pdf
ankushspencer015
Call Girls Walvekar Nagar Call Me 7737669865 Budget Friendly No Advance Booking
Call Girls Walvekar Nagar Call Me 7737669865 Budget Friendly No Advance Booking
roncy bisnoi
Online banking management system project.pdf
Online banking management system project.pdf
Kamal Acharya
Bhosari ( Call Girls ) Pune 6297143586 Hot Model With Sexy Bhabi Ready For ...
Bhosari ( Call Girls ) Pune 6297143586 Hot Model With Sexy Bhabi Ready For ...
tanu pandey
Generative AI or GenAI technology based PPT
Generative AI or GenAI technology based PPT
bhaskargani46
(INDIRA) Call Girl Aurangabad Call Now 8617697112 Aurangabad Escorts 24x7
(INDIRA) Call Girl Aurangabad Call Now 8617697112 Aurangabad Escorts 24x7
Call Girls in Nagpur High Profile Call Girls
Top Rated Call Girls In chittoor 📱 {7001035870} VIP Escorts chittoor
Top Rated Call Girls In chittoor 📱 {7001035870} VIP Escorts chittoor
dharasingh5698
Call Girls in Netaji Nagar, Delhi 💯 Call Us 🔝9953056974 🔝 Escort Service
Call Girls in Netaji Nagar, Delhi 💯 Call Us 🔝9953056974 🔝 Escort Service
9953056974 Low Rate Call Girls In Saket, Delhi NCR
Unit 2- Effective stress & Permeability.pdf
Unit 2- Effective stress & Permeability.pdf
RagavanV2
Intro To Electric Vehicles PDF Notes.pdf
Intro To Electric Vehicles PDF Notes.pdf
rs7054576148
Dernier
(20)
NFPA 5000 2024 standard .
NFPA 5000 2024 standard .
data_management_and _data_science_cheat_sheet.pdf
data_management_and _data_science_cheat_sheet.pdf
Booking open Available Pune Call Girls Pargaon 6297143586 Call Hot Indian Gi...
Booking open Available Pune Call Girls Pargaon 6297143586 Call Hot Indian Gi...
CCS335 _ Neural Networks and Deep Learning Laboratory_Lab Complete Record
CCS335 _ Neural Networks and Deep Learning Laboratory_Lab Complete Record
Call Girls Wakad Call Me 7737669865 Budget Friendly No Advance Booking
Call Girls Wakad Call Me 7737669865 Budget Friendly No Advance Booking
VIP Model Call Girls Kothrud ( Pune ) Call ON 8005736733 Starting From 5K to ...
VIP Model Call Girls Kothrud ( Pune ) Call ON 8005736733 Starting From 5K to ...
Unit 1 - Soil Classification and Compaction.pdf
Unit 1 - Soil Classification and Compaction.pdf
VIP Call Girls Ankleshwar 7001035870 Whatsapp Number, 24/07 Booking
VIP Call Girls Ankleshwar 7001035870 Whatsapp Number, 24/07 Booking
ONLINE FOOD ORDER SYSTEM PROJECT REPORT.pdf
ONLINE FOOD ORDER SYSTEM PROJECT REPORT.pdf
chapter 5.pptx: drainage and irrigation engineering
chapter 5.pptx: drainage and irrigation engineering
AKTU Computer Networks notes --- Unit 3.pdf
AKTU Computer Networks notes --- Unit 3.pdf
Call Girls Walvekar Nagar Call Me 7737669865 Budget Friendly No Advance Booking
Call Girls Walvekar Nagar Call Me 7737669865 Budget Friendly No Advance Booking
Online banking management system project.pdf
Online banking management system project.pdf
Bhosari ( Call Girls ) Pune 6297143586 Hot Model With Sexy Bhabi Ready For ...
Bhosari ( Call Girls ) Pune 6297143586 Hot Model With Sexy Bhabi Ready For ...
Generative AI or GenAI technology based PPT
Generative AI or GenAI technology based PPT
(INDIRA) Call Girl Aurangabad Call Now 8617697112 Aurangabad Escorts 24x7
(INDIRA) Call Girl Aurangabad Call Now 8617697112 Aurangabad Escorts 24x7
Top Rated Call Girls In chittoor 📱 {7001035870} VIP Escorts chittoor
Top Rated Call Girls In chittoor 📱 {7001035870} VIP Escorts chittoor
Call Girls in Netaji Nagar, Delhi 💯 Call Us 🔝9953056974 🔝 Escort Service
Call Girls in Netaji Nagar, Delhi 💯 Call Us 🔝9953056974 🔝 Escort Service
Unit 2- Effective stress & Permeability.pdf
Unit 2- Effective stress & Permeability.pdf
Intro To Electric Vehicles PDF Notes.pdf
Intro To Electric Vehicles PDF Notes.pdf
An Optimized Parallel Algorithm for Longest Common Subsequence Using Openmp – A Review
1.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 03 Issue: 02 | Feb-2016 www.irjet.net p-ISSN: 2395-0072 © 2016, IRJET ISO 9001:2008 Certified Journal Page 1174 “AN OPTIMIZED PARALLEL ALGORITHM FOR LONGEST COMMON SUBSEQUENCE USING OPENMP” – A Review 1Hanok Palaskar, 2Prof. Tausif Diwan 1 M.Tech Student, CSE Department, Shri Ramdeobaba College of Engineering and Management, Nagpur, India 2Assistant Professor, CSE Department, Shri Ramdeobaba College of Engineering and Management, Nagpur, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - The LCS problem is to find the maximum length common subsequence of two or more given sequences. Finding the Longest Common Subsequence has many applications in the areas of bioinformatics and computational genomics. LCS problem has optimal substructure and overlapping sub problems, problems with such properties can be approached by a dynamic programming problem solving technique. Due to growth of database sizes of biological sequences, parallel algorithms are the best solution to solve these large size problems in less amount of time than sequential algorithms. In this paper we have carried out a brief survey of different parallel algorithms and approaches to parallelize LCS problem on the multi-core CPUs and as well as on GPUs. We have also proposed our optimized parallel algorithm to solve LCS problem on multi-core CPUs using a tool OpenMP. Key Words: LCS, Dynamic Programming, Parallel Algorithm, OpenMP. 1. INTRODUCTION One of the classical problems in computer science is the longest common subsequence. In LCS problem we are given two sequences A = (a1, a2,….am) and B = (b1,b2,….bn) and wish to find a maximum length common subsequence of A and B. By using the Dynamic Programming technique LCS can be solved in O(mn) time. Dynamic Programming algorithms recursively break the problem up into overlapping sub problems, and store the answer to the sub problems for later reference. If there is an enough overlapping of sub problems, then the time complexity can be reduced drastically, typically from exponential to polynomial. LCS has the application in many areas, such as speech recognition, file comparison, and especially bioinformatics. Most common studies in the bioinformatics field have evolved towards a more large scale, for example, analysis and study of genome/proteome instead of a single gene/protein. Hence, it becomes more and more difficult to achieve these analyses using classical sequential algorithms on a single computer. The bioinformatics requires now parallel algorithms for the massive computation for their analysis. Parallel algorithms, are different from a traditional serial algorithms, and can be executed a piece at a time on many different processing devices, and at the end to get the correct result can be combined together. Due to the spread of multicore machines and multithreading processors in the marketplace, we can create parallel programs for uniprocessor computers also, and can be used to solve large scale instances problems like LCS. One of the best tools to do parallel processing on multi-core CPUs is OpenMP, which is a shared-memory application programming interface (API) and can be used to describe how the work is to be shared among threads that will execute on different processors or cores. 2. THE LONGEST COMMON SUBSEQUENCE PROBLEM The deduction of longest common subsequence of two or more sequences is a current problem in the domain of bioinformatics, pattern matching and data mining. The deduction of these subsequences is frequently used as a technique of comparison in order to get the similarity degree between two or more sequences. 2.1 Definition A sequence is a finite set of characters or symbols. If P = <a1,a2, ..,an> is a sequence, where a1,a2, ..,an are characters, the integer n is the magnitude of P. A sequence Q = <b1,b2, ..,bn> is a subsequence of P = <a1,a2, ..,an> if there are integers i1, i2, .., im(1 ≤ i1 < i2 < .. < im ≤ n) where bk = aik for k ∈ [1,m]. For example, X = <B,C,D,E> is a subsequence of Y = <A,B,C,D,E,F>. A sequence W is a common
2.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 03 Issue: 02 | Feb-2016 www.irjet.net p-ISSN: 2395-0072 © 2016, IRJET ISO 9001:2008 Certified Journal Page 1175 subsequence to sequences X and Y if W is a subsequence of X and of Y. A common subsequence is largest subsequence if it is having maximum length. For example: sequences <C,D,C,B> and <C,E,B,C> are the longest common subsequences of <B,C,D,C,E,B,C> and of <C,E,D,B,C,B>. 2.2 Score Matrix A classical approach for solving the LCS problem is the dynamic programming. This technique is based on the filling of a score matrix by using a scoring mechanism. The last calculated score is the length of the LCS and by tracing back the table we can get the subsequence. Consider n and m be the lengths of two strings which are to be compared. We determine the length of a largest common subsequence in X = <x1,x2, ..,xn> and Y = <y1,y2, ..,ym>. We find L(i, j) the length of a largest common subsequence in <a1,a2, ..ai> and <b1,b2, ..,bj>(0 ≤ j ≤m,0 ≤ i ≤n). 0 if i=0 or j=0 L(i,j) = L(i-1,j-1) + 1 if ai = bj Max(L(i,j-1), L(i-1,j)) else We use the above scoring function in order to fill the matrix row by row (fig. 1). Fig. 1: Example of Filling LCS Score Matrix The length of the LCS is the highest calculated score in the score matrix. In Fig. 1, the length is 4. To find the LCS we trace back from the highest score point (4) to the score 1 in the score matrix. 3. LITERATURE SURVEY To find an optimized solution, lot of research is going on for over thirty years for the LCS problem. The solutions are based on Dynamic Programming, Divide-and-conquer, or Dominant Point technique etc. also, many attempts has been made to parallelize the existing algorithms. This section briefly explains the techniques and approaches used by the various authors to parallelize the LCS problem in order to get the optimized solution. Amine Dhraief, Raik Issaoui and Abdelfettah Belghith in “Parallel Computing the Longest Common Subsequence (LCS) on GPUs: Efficiency and Language Suitability” focused on parallelization of LCS problem using Dynamic Programming approach. They have presented parallelization approach for solving LCS problem on GPU, and implemented their proposed algorithm on NVIDIA platform using CUDA and OpenCL. To parallelize their algorithm they have computed the score matrix in the anti-diagonal direction instead of row wise. They have also implemented their proposed algorithm on CPU using OpenMP API. Their algorithm achieves good speedup on GPU than CPU, and for their proposed algorithm CUDA is more suitable, for NVIDIA platform, than OpenCL[1]. Quingguo Wang, Dmitry Korkin, and Yi Shang in “A Fast Multiple Longest Common Subsequnce (MLCS) Algorithm” presented an algorithm for general case of Multiple LCS and its parallelization. Their algorithm is based on dominant point approach and a fast divide-and- conquer technique is used to compute the dominant points. The parallelization of the algorithm is carried out using multiple processors having one master processor and other as slaves. Master processor starts the divide-and-conquer algorithm, splits the dominant points and assigns them evenly to two children processor. The program is recursively executed at the children processor to form binary tree based on parent- children relationship. This algorithm shows asymptotically linear speed-up with respect to sequential algorithm [2].
3.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 03 Issue: 02 | Feb-2016 www.irjet.net p-ISSN: 2395-0072 © 2016, IRJET ISO 9001:2008 Certified Journal Page 1176 Amit Shukla and Suneeta Agrawal in “A Relative Position based Algorithm to find out the Longest Common Subsequence from Multiple Biological Sequnces” proposed a parallel algorithm for LCS based on the calculation of the relative positions of the characters from any number of given sequences. The speed-up in this algorithm has been achieved by recognizing and rejecting all those subsequences which cannot generate the next character of the LCS. For this algorithm it is required to have the number of characters being used in the sequences in advance. Parallelization approach of this algorithm uses multiple processors where number of processors is equal to the number of characters in the finite symbol set. Calculations are done at each processor and the results are stored at local memories of each processer which are then combined to get the final LCS. The complexity for the sequential algorithm is O(n) where n is length of the sequence and the complexity for the parallel algorithm is O(k) where k is the slowest processor. Complexity for the parallel algorithm is independent of the number of sequences [3]. R. Devika Ruby and Dr. L. Arockiam in “Positional LCS: A position based algorithm to find Longest Common Subsequence (LCS) in Sequence Database (SDB)” in their paper presented a position based algorithm for LCS which is useful in Sequence Database Applications. Their proposed algorithm focuses only on matched position, instead of unmatched positions of sequences, to get LCS. Primary idea for their algorithm is to remove backtracking time by storing only the matched position, where LCS occurs. Also to reduce the time complexity, instead of searching entire score matrix, matched position array is used. In their proposed algorithm score matrix is computed for entire sequences, but to find the LCS their algorithm scans only the matched positions. Time complexity of their proposed algorithm is reduced to half than time complexity of dynamic LCS [4]. Jaimei Liu and Suping Wu in “Research on Longest Common Subsequence Fast Algorithm” proposed a fast algorithm for LCS, for two sequences having length ‘m’ and ‘n’, by transforming the problem of LCS into solving the problem of matrix m[p,m] (where p< min(m,n)). They have also presented the parallelization of their proposed algorithm based on OpenMP. For optimizing computation of each element in the score matrix, they have used their proposed theorems. To find the LCS instead of backtracking the score matrix, they have used a simple formula which gives the required LCS in constant time. Time complexity of their proposed algorithm is O(np) and the space complexity is O(m+n). They have realized the parallelization of their proposed algorithm by using OpenMP constructs on the nested outer loops [5]. 4. PROPOSED WORK We propose the new optimized parallel LCS algorithm. Major factor in finding the LCS is the computation of score matrix hence we will optimize the calculation of elements in the score matrix by using theorems, instead of classical method. We will implement our parallel algorithm on the multi-core CPUs using OpenMP API constructs. To increase the performance of our parallel in terms of speed and memory optimization we will divide the load among the threads by applying load balancing techniques and cache optimization respectively. We expect that our proposed algorithm will be faster than the existing Parallel LCS algorithms. In future we will expand our algorithm, to support the Multiple Longest Common Subsequence (MLCS) and also to make the hybrid version of our algorithm by combining OpenMP and MPI. 5. CONCLUSION Problem of LCS have the variety of applications in the domain of pattern matching, data mining and bioinformatics. Due to the recent developments in the multi-core CPUs, parallel algorithms using OpenMP are one of the best ways to solve the problems having large size inputs. This paper presented a review of parallel algorithm for the Longest Common Subsequence problem and approaches to parallelize LCS problem on the multi-core CPUs and as well as on GPUs. We also have proposed our parallel algorithm and the optimizations in order to increase the performance, which we expect to be the faster algorithm in comparison to the existing parallel algorithms for solving LCS.
4.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 03 Issue: 02 | Feb-2016 www.irjet.net p-ISSN: 2395-0072 © 2016, IRJET ISO 9001:2008 Certified Journal Page 1177 6. REFERENCES [1] Amine Dhraief, Raik Issaoui, Abdelfettah Belghith, “Parallel Computing the Longest Common Subsequence (LCS) on GPUs: Efficiency and Language Suitability”, The First International Conference on Advanced Communications and Computation, 2011. [2] Quingguo Wang, Dmitry Korkin, Yi Shang, “A Fast Multiple Longest Common Subsequnce (MLCS) Algorithm”,IEEE transaction on knowledge and data engineering, 2011. [3] Amit Shukla, Suneeta Agrawal, “A Relative Position based Algorithm to find out the Longest Common Subsequence from Multiple Biological Sequnces ”, 2010 International Conference on Computer and Communication Technology, pages 496 – 502. [4]R. Devika Ruby, Dr. L. Arockiam, “Positional LCS: A position based algorithm to find Longest Common Subsequence (LCS) in Sequence Database (SDB)”, IEEE International Conference on Computational Intelligence and Computing Research, 2012. [5] Jiamei Liu, Suping Wu “Research on Longest Common Subsequence Fast Algorithm”, 2011 International Conference on Consumer Electronics, Communications and Networks, pages 4338 – 4341. [6] Zhong Zheng, Xuhao Chen, Zhiying Wang, Li Shen, Jaiwen Li “Performance Model for OpenMP Parallelized Loops ”, 2011 International Conference on Transportation, Mechanical and Electrical Engineering (TMEE), pages 383-387. [7] Rahim Khan, Mushtaq Ahmad, Muhammad Zakarya, “Longest Common Subsequence Based Algorithm for Measuring Similarity Between Time Series: A New Approach” World Applied Sciences Journal, pages 1192-1198. [8] Jiaoyun Yang, Yun Xu,“A Space-Bounded Anytime Algorithm for the Multiple Longest Common Subsequence Problem”, IEEE transaction on knowledge and data engineering, 2014. [9] I-Hsuan Yang, Chien-Pin Huang, Kun-Mao Chao, “A fast algorithm for computing a longest common increasing subsequence”, Information Processing Letters, ELSEVIER, 2004. [10] Yu Haiying, Zhao Junlan, Application of Longest Common Subsequence Algorithm in Similarity Measurement of Program Source Codes. Journal of Inner Mongolia University, vol. 39, pp. 225–229, Mar 2008. [11] Krste Asanovic, Ras Bodik, Bryan, Joseph, Parry, Samuel Williams, “The Landscape of Parallel Computing Research: A view from Berkeley” Electrical Engineering and Computer Sciences University of California at Berkeley, December 2006. [12] Barbara Champman, Gabriel Jost, Ruud Van Der Pas “Using OpenMP”, 1-123
Télécharger maintenant