CPUs vs GPUs: Key Differences You Must Understand (HL)

GPUs excel at tasks where the same instruction is applied repeatedly to different data values.

Key Architectural Differences

The main difference between CPUs and GPUs lies in how they are built.

• CPUs prioritise:
  • Control logic
  • Branching
  • Low-latency execution
• GPUs prioritise:
  • Arithmetic units
  • Throughput
  • Massive parallelism

A CPU might have 8 powerful cores, while a GPU might have thousands of simpler cores.

When CPUs Are Better Than GPUs

CPUs are better for:

• Running operating systems
• Handling user input
• Managing program flow
• Executing conditional logic
• Tasks that require frequent branching

These tasks benefit from strong control and flexibility.

When GPUs Are Better Than CPUs

GPUs are better for:

• Graphics rendering
• Image and video processing
• Scientific simulations
• Machine learning
• Data-parallel computations

In these cases, GPUs can process large datasets much faster than CPUs.

CPUs, GPUs, and IB Exam Questions

In IB Computer Science HL, students may be asked to:

• Compare CPUs and GPUs
• Explain why GPUs are used in AI or graphics
• Link GPU architecture to performance
• Discuss advantages and limitations

Clear comparisons using architecture and purpose are rewarded.

Common Student Mistakes

Students often:

• Say GPUs are simply “faster CPUs”
• Ignore parallelism
• Forget CPU control roles
• Confuse clock speed with overall performance

Performance depends on task type, not just speed.

Final Thoughts

CPUs and GPUs are designed for different kinds of processing. CPUs handle complex, sequential tasks, while GPUs excel at parallel workloads involving large datasets. Modern computers use both to maximise performance.

Understanding this distinction helps IB HL students explain real-world computing systems accurately — and score higher in performance-related questions.

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