Parallel complexity theory

What are the benefits of parallel processing?
Benefits of parallel computing.
The advantages of parallel computing are that computers can execute code more efficiently, which can save time and money by sorting through “big data” faster than ever.
Parallel programming can also solve more complex problems, bringing more resources to the table..
What is complexity analysis of parallel algorithms?
In computer science, the analysis of parallel algorithms is the process of finding the computational complexity of algorithms executed in parallel – the amount of time, storage, or other resources needed to execute them..
What is the parallel computing complexity theory?
In computational complexity theory, the parallel computation thesis is a hypothesis which states that the time used by a (reasonable) parallel machine is polynomially related to the space used by a sequential machine.
The parallel computation thesis was set forth by Chandra and Stockmeyer in 1976..
What is the theory of parallel computing?
Parallel computing refers to the process of breaking down larger problems into smaller, independent, often similar parts that can be executed simultaneously by multiple processors communicating via shared memory, the results of which are combined upon completion as part of an overall algorithm..
Where are parallel algorithms used?
Parallelism can be implemented by using parallel computers, i.e. a computer with many processors.
Parallel computers require parallel algorithm, programming languages, compilers and operating system that support multitasking..
Why do we need parallel algorithms?
Parallel algorithms are methods for organizing the computational work of a given application such that multiple parts of the workload can be performed concurrently to reduce the time to solution and increase performance..
In computer science, the analysis of parallel algorithms is the process of finding the computational complexity of algorithms executed in parallel – the amount of time, storage, or other resources needed to execute them.
Parallel Algorithm − The problem is divided into sub-problems and are executed in parallel to get individual outputs.
Later on, these individual outputs are combined together to get the final desired output.

Jan 25, 2021The parallel feasibility usually means decidability in polynomial time with polynomial number of processors. So a problem is parallel feasible

Researchers have developed a theory of the parallel complexity of computational problems analogous to the theory of NP-completeness. A problem is said to belong to the class NC (Nick's Class) if it can be solved in time polylogarithmic in the size of the problem using at most a polynomial number of processors.

The parallel complexity is Tp(N)= O(logp+IN ) [k2,S1]; so, P processors achieve a speedup of e(log(P +1)). Thus, a number of processors that is polynomial in the sequential time t = O(logN) cannot achieve running time polylogarithmic in t. Similar trade-offs have been exhibited for other search problems [KW].

Type of chord

Parallel complexity theory

What are the benefits of parallel processing?

What is complexity analysis of parallel algorithms?

What is the parallel computing complexity theory?

What is the theory of parallel computing?

Where are parallel algorithms used?

Why do we need parallel algorithms?