Parallel method invocation (PMI) is a computational concept used in programming and Big Data analytics which allows an application to call or invoke functions or methods in parallel, as opposed to one after the other which is the norm in most instances.
The core concept is for a single caller to invoke the same function multiple times in parallel to allow for parallel execution and speed up whatever the process is meant to accomplish.
Parallel method invocation is simply invoking multiple methods or functions in parallel; there may be multiple ways of achieving this depending on the application and the requirements. Currently the model is being implemented in programming and development notably in Python. The concept is also extensively used Big Data analytics to allow the servers to generate reports faster.
In Python programming, PMI is a module which helps foster parallel computing by joining together the fork-join model and message passing concepts for process communication in parallel computing. In Big Data Analytics, analytics providers and programmers have implemented a PMI method through an API which allows the application to call map/reduce methods in parallel and have them executed in different grid servers in parallel and the results merged afterward. This significantly reduces report generation time.
The core concept is for a single caller to invoke the same function multiple times in parallel to allow for parallel execution and speed up whatever the process is meant to accomplish.
Parallel method invocation is simply invoking multiple methods or functions in parallel; there may be multiple ways of achieving this depending on the application and the requirements. Currently the model is being implemented in programming and development notably in Python. The concept is also extensively used Big Data analytics to allow the servers to generate reports faster.
In Python programming, PMI is a module which helps foster parallel computing by joining together the fork-join model and message passing concepts for process communication in parallel computing. In Big Data Analytics, analytics providers and programmers have implemented a PMI method through an API which allows the application to call map/reduce methods in parallel and have them executed in different grid servers in parallel and the results merged afterward. This significantly reduces report generation time.
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