Automatic parallelization – link to Data dependence

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* Is it worthwhile to parallelize it? This answer requires a reliable estimation (modeling) of the program workload and the capacity of the parallel system.
* Is it worthwhile to parallelize it? This answer requires a reliable estimation (modeling) of the program workload and the capacity of the parallel system.


The first pass of the compiler performs a [[dependence analysis|data dependence analysis]] of the loop to determine whether each iteration of the loop can be executed independently of the others. Data dependence can sometimes be dealt with, but it may incur additional overhead in the form of [[message passing]], synchronization of [[shared memory (interprocess communication)|shared memory]], or some other method of processor communication.
The first pass of the compiler performs a [[data dependence]] analysis of the loop to determine whether each iteration of the loop can be executed independently of the others. Data dependence can sometimes be dealt with, but it may incur additional overhead in the form of [[message passing]], synchronization of [[shared memory (interprocess communication)|shared memory]], or some other method of processor communication.


The second pass attempts to justify the parallelization effort by comparing the theoretical execution time of the code after parallelization to the code's sequential execution time. Somewhat counterintuitively, code does not always benefit from parallel execution. The extra overhead that can be associated with using multiple processors can eat into the potential speedup of parallelized code.
The second pass attempts to justify the parallelization effort by comparing the theoretical execution time of the code after parallelization to the code's sequential execution time. Somewhat counterintuitively, code does not always benefit from parallel execution. The extra overhead that can be associated with using multiple processors can eat into the potential speedup of parallelized code.