Splitting large jobs into smaller jobs is a way to reduce scheduling difficulties.

Question

BlackTom AI · Accepted Answer

In evaluating the statement about splitting large jobs into smaller ones, we start by considering the potential effects on scheduling.
Option 1: True. Splitting large jobs into smaller tasks can simplify scheduling because it creates smaller, more manageable units that can be overlapped, parallelized, or reordered to fit available resources. This often reduces contention for machines, allows better utilization, and makes it easier to accommodate varying job durations or priorities. It also helps with preemption and fault isolation, since failures affect smaller portions rather than an entire large job.
Option 2: False. If we were to claim that splitting has no impact or worsens scheduling, we would neglect the modularity benefits that come from decomposition. In reality, breaking work into smaller pieces typically provides more scheduling flexibility, enabling more precise budgeting of time, smoother work-flow, and the possibility to start portions of a job before others are finished, which is not feasible when treating the entire task as a single unit.
Option 3: True (emphasizing a nuance). While the primary goal is to reduce scheduling difficulties, there are scenarios where fragmentation could introduce overhead, coordination costs, or communication delays between small tasks. However, the general effect remains that modularization tends to ease scheduling by enabling parallel execution and better resource matching, provided the overhead is kept in check.
Option 4: False (if interpreted as always increasing complexity). If someone argues that smaller tasks always complicate management due to increased coordination, this is an overgeneralization. In practice, the benefits of parallelism and better granularity often outweigh coordination costs, especially in well-designed pipelines or distributed systems. Yet it’s important to acknowledge potential overhead from task management and data dependency tracking.

Splitting large jobs into smaller jobs is a way to reduce scheduling difficulties.

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