Review Article
Survey of Scientific Programming Techniques for the Management of Data-Intensive Engineering Environments
Table 3
Mapping between methods/models/techniques and hardware domains.
| | | GPU | FPGA | Multiprocessor | Grid computing |
| Artificial intelligence | Deep learning | | | | | Fuzzy logic | | | | | Gene programming | | | | | General techniques | | | | | Neural networks | | | | | Planning | | | | |
| Computational architecture | Infrastructure | | | [40] | | Scheduling techniques | | | | | Workflow | | | | |
| Computation model | Event calculus | | | | | MPI | | | | | Parallel programming | [41, 42] | | | | Query distribution | | | | | Stream processing | [43] | | | |
| Computational science | Euler models | ([44], p. 2) | | | | Scientific computation | | | | | Statistical methods | | | | |
| Graph theory | Automata | | | | | Complex network analysis | | | | |
| Engineering | Finite elements | [45] | | | | Simulation | [46, 47] | | | |
| Machine learning | Bayesian machine learning | [48] | | | | Data mining | | | | | Information fusion | | | | | Pattern recognition | [49, 50] | | [51] | [52] | Predictive models | | | | | Support vector machines | | [53] | | | Regression model | | | | |
| Mathematics and applied mathematics | Gradient descent search | | | | | Integer linear programming | | | | | Linear algebra/solvers | [54–56] | | [57] | | Linear programming | | | | | Matrix calculation | [54–56, 58] | | [59] | | Nonlinear programming | | | | | Numerical methods | | | | | Symbolic execution | | | | |
| Programming techniques | Constraint programming | | | | | Cube computation | [60] | | | | Dynamic programming | | | | | DSL | | | | | Stochastic programming | | | | |
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