Table of Contents
What factors limit the speed up of parallel algorithms?
The speedup is limited by the serial part of the program. For example, if 95\% of the program can be parallelized, the theoretical maximum speedup using parallel computing would be 20 times.
What are the features of parallel algorithm?
A parallel algorithm is an algorithm that can execute several instructions simultaneously on different processing devices and then combine all the individual outputs to produce the final result.
Why parallel algorithms reach a limit?
Because some portions are always still sequential, the benefits of adding more processors will go down and eventually the speedup reaches a limit.
What are the benefits and challenges of parallel computing?
Benefits of parallel computing
- Parallel computing models the real world. The world around us isn’t serial.
- Saves time. Serial computing forces fast processors to do things inefficiently.
- Saves money. By saving time, parallel computing makes things cheaper.
- Solve more complex or larger problems.
- Leverage remote resources.
Why do parallel algorithms have a limit?
Often times a parallel solution will be the fastest option, but there is a limit. Solutions that use parallel computing can scale more effectively than solutions that use sequential computing. Why is this so? If we continue to add tasks, a sequential solution would continue to get larger and larger.
What do you mean by speedup in sequential and parallel algorithms give an example?
The speedup of a parallel algorithm over a corresponding sequential algorithm is the ratio of the compute time for the sequential algorithm to the time for the parallel algorithm. For example, in the solar system computation, results need to be copied across the network upon every iteration.
What are the factors that influence parallel processing?
The performance of any parallel application is ultimately bounded by the speed, capacity and interfaces of each processing element. Programming a parallel computer depends on how the memory of the hardware platform is organized or divided among the processors.
What are the important characteristics of parallel algorithms?
The data set is organized into some structure like an array, hypercube, etc. Processors perform operations collectively on the same data structure. Each task is performed on a different partition of the same data structure. It is restrictive, as not all the algorithms can be specified in terms of data parallelism.