Sachar et al., 2016 - Google Patents
Genetic algorithm using MapReduce-A critical reviewSachar et al., 2016
View PDF- Document ID
- 18311516098158915417
- Author
- Sachar P
- Khullar V
- Publication year
- Publication venue
- i-manager‟ s Journal on Cloud Computing
External Links
Snippet
Now a days, to get an optimize solution of hard problems is a biggest challenge. Scientists are putting their best efforts to introduce best algorithm to optimize the problem to a great extent. Genetic Algorithm is one of the stepping stone in the challenge and is an …
- 238000004422 calculation algorithm 0 title abstract description 68
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for programme control, e.g. control unit
- G06F9/06—Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5061—Partitioning or combining of resources
- G06F9/5066—Algorithms for mapping a plurality of inter-dependent sub-tasks onto a plurality of physical CPUs
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for programme control, e.g. control unit
- G06F9/06—Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
- G06F9/5027—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals
- G06F9/505—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals considering the load
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F15/00—Digital computers in general; Data processing equipment in general
- G06F15/16—Combinations of two or more digital computers each having at least an arithmetic unit, a programme unit and a register, e.g. for a simultaneous processing of several programmes
- G06F15/163—Interprocessor communication
- G06F15/173—Interprocessor communication using an interconnection network, e.g. matrix, shuffle, pyramid, star, snowflake
- G06F15/17306—Intercommunication techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
- G06F17/30386—Retrieval requests
- G06F17/30424—Query processing
- G06F17/30533—Other types of queries
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for programme control, e.g. control unit
- G06F9/06—Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
- G06F9/46—Multiprogramming arrangements
- G06F9/48—Programme initiating; Programme switching, e.g. by interrupt
- G06F9/4806—Task transfer initiation or dispatching
- G06F9/4843—Task transfer initiation or dispatching by program, e.g. task dispatcher, supervisor, operating system
- G06F9/4881—Scheduling strategies for dispatcher, e.g. round robin, multi-level priority queues
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/12—Computer systems based on biological models using genetic models
- G06N3/126—Genetic algorithms, i.e. information processing using digital simulations of the genetic system
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F7/00—Methods or arrangements for processing data by operating upon the order or content of the data handled
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F2217/00—Indexing scheme relating to computer aided design [CAD]
- G06F2217/78—Power analysis and optimization
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F8/00—Arrangements for software engineering
- G06F8/40—Transformations of program code
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F2209/00—Indexing scheme relating to G06F9/00
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Rashidi et al. | Astra-sim: Enabling sw/hw co-design exploration for distributed dl training platforms | |
Iranmanesh et al. | DCHG-TS: a deadline-constrained and cost-effective hybrid genetic algorithm for scientific workflow scheduling in cloud computing | |
US8943011B2 (en) | Methods and systems for using map-reduce for large-scale analysis of graph-based data | |
US9053067B2 (en) | Distributed data scalable adaptive map-reduce framework | |
Agrawal et al. | Transit route network design using parallel genetic algorithm | |
Pattabiraman et al. | A parallel Monte Carlo code for simulating collisional N-body systems | |
Guan et al. | A hybrid parallel cellular automata model for urban growth simulation over GPU/CPU heterogeneous architectures | |
Fiore et al. | On the road to exascale: Advances in High Performance Computing and Simulations—An overview and editorial | |
Talbi | A unified view of parallel multi-objective evolutionary algorithms | |
Talbi et al. | Metaheuristics on gpus | |
Hussain et al. | Energy efficient real-time tasks scheduling on high-performance edge-computing systems using genetic algorithm | |
Ma et al. | Multidimensional parallel dynamic programming algorithm based on spark for large-scale hydropower systems | |
Dornala et al. | Quantum based Fault-Tolerant Load Balancing in Cloud Computing with Quantum Computing | |
Sachar et al. | Genetic algorithm using MapReduce-A critical review | |
Guo et al. | Hierarchical design space exploration for distributed CNN inference at the edge | |
Sachar et al. | Social media generated big data clustering using genetic algorithm | |
Atrushi et al. | Distributed Graph Processing in Cloud Computing: A Review of Large-Scale Graph Analytics | |
Makarov et al. | Agent-based supercomputer demographic model of Russia: approbation analysis | |
Jiao et al. | Molecular dynamics simulation: Implementation and optimization based on Hadoop | |
Zawalska et al. | Leveraging Hybrid Classical-Quantum Methods for Efficient Load Rebalancing in HPC | |
Remis et al. | Exploiting social network graph characteristics for efficient BFS on heterogeneous chips | |
Luo et al. | Optimizing Task Placement and Online Scheduling for Distributed GNN Training Acceleration in Heterogeneous Systems | |
Islam et al. | A Heuristic Approach for Optimizing Travel Planning Using Genetics Algorithm | |
Moukir et al. | From MATSim to MultiMATSim: Rethinking Traffic Modeling Using the ‘Unite and Conquer’Approach | |
Xu et al. | EdgeMesh: A hybrid distributed training mechanism for heterogeneous edge devices |