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To determine the highest possible increase in speed by implementing parallelization, you need to consider several factors, including:

1. The nature of the task: Parallelization works best for tasks that can be broken down into smaller, independent sub-tasks that can be executed simultaneously.

2. The number and type of processors: The more processors you have, the more potential for parallelization. Different processors have different capabilities, and you need to choose the right type of processors for your task.

3. The efficiency of the parallelization algorithm: The algorithm used to parallelize the task needs to be efficient and well-tuned to avoid overheads and delays.

4. The amount of data being processed: The more data being processed, the greater the potential benefit from parallelization.

5. The communication overhead: Parallelization requires communication between processors, which can create overhead and delay the performance. Minimizing the communication overhead is critical for achieving the highest possible increase in speed.

By considering these factors, you can estimate the highest possible speedup that parallelization can provide for a given task. However, achieving the estimated speedup depends on the effective implementation of the parallelization technique.