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
Famous quotes containing the word methods:
“The comparison between Coleridge and Johnson is obvious in so far as each held sway chiefly by the power of his tongue. The difference between their methods is so marked that it is tempting, but also unnecessary, to judge one to be inferior to the other. Johnson was robust, combative, and concrete; Coleridge was the opposite. The contrast was perhaps in his mind when he said of Johnson: his bow-wow manner must have had a good deal to do with the effect produced.”
—Virginia Woolf (18821941)