Algorithmic Methods
As parallel computers become larger and faster, it becomes feasible to solve problems that previously took too long to run. Parallel computing is used in a wide range of fields, from bioinformatics (protein folding and sequence analysis) to economics (mathematical finance). Common types of problems found in parallel computing applications are:
- Dense linear algebra
- Sparse linear algebra
- Spectral methods (such as Cooley–Tukey fast Fourier transform)
- n-body problems (such as Barnes–Hut simulation)
- Structured grid problems (such as Lattice Boltzmann methods)
- Unstructured grid problems (such as found in finite element analysis)
- Monte Carlo simulation
- Combinational logic (such as brute-force cryptographic techniques)
- Graph traversal (such as sorting algorithms)
- Dynamic programming
- Branch and bound methods
- Graphical models (such as detecting hidden Markov models and constructing Bayesian networks)
- Finite-state machine simulation
Read more about this topic: Parallel Computing
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