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- n. The condition of being parallelizable
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For parallel applications, valid test objectives include understanding the parallelizability / scalability of the algorithm used, understanding the performance characteristics of various design alternatives, discovering the synchronizations and communication overheads, and validating that performance requirements are met.
The scalability exhibited by the application really depends on the parallelizability of the algorithm used.
Other algorithms may not produce embarrassingly parallel speedup due to limited parallelizability, resulting in total runtime speed increases that aren't proportional to the number of CPUs.
As postulated in Amdahl's law and Gustafson's law, the performance gain achieved by a parallel application is greatly influenced by its algorithm's parallelizability aspects, amount of sequential parts in the program, the parallelization overhead and the data / workload characteristics.
For example, customers deploying a parallel application would perform performance testing to ensure that business needs are met, while the development team would be interested in conducting exhaustive performance testing to identify bottlenecks and to improve the parallelizability of the program.