Why might one want to adjust the "parallelism" parameter in SparkTrials?

The adjustment of the "parallelism" parameter in SparkTrials is primarily aimed at balancing resource utilization and training time. By fine-tuning this parameter, one can optimize how many trials are run in parallel, which directly influences both the efficiency of resource usage and the speed of the training process.

Increasing the parallelism allows multiple iterations or trials to be processed at the same time, potentially leading to faster convergence and quicker overall model training. However, if parallelism is set too high relative to the available resources (like CPU or memory), it may lead to contention for those resources, ultimately slowing down the training process. Conversely, setting it too low can result in underutilization of available resources, making the training process unnecessarily prolonged.

Thus, the ability to adjust parallelism helps practitioners to effectively manage the computational workload within their environment, ensuring that training proceeds efficiently while maximizing the use of the available hardware.