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