Parallel Computing - Algorithmic Methods

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