Job Optimizer uses parallelism to reduce the elapsed time of multi-step batch jobs. For jobs that match specified criteria, Job Optimizer analyzes the execution characteristics of the job's steps during normal sequential processing.

Based on this analysis, and if no constraints exist, Job Optimizer can split the job steps into separate units of work. Job Optimizer can then run the split job steps in parallel on the same image, or on separate images, during subsequent runs of the job.

Job Optimizer automatically applies its step parallelism functions to batch jobs that meet user-specified criteria and whose history of job step behavior indicate that the steps can be run in parallel.

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