The big idea: A CPU and a GPU are both processors. Neither is better: they are built for opposite shapes of work.
CPU
- A few powerful cores
- Built to run one instruction stream as fast as possible
GPU
- Thousands of simple cores
- Built to run the same operation on thousands of values at once
| CPU | GPU | |
|---|---|---|
| Cores | A few — typically 4 to 16 | Thousands, each far simpler |
| Optimised for | Latency — finish one task fast | Throughput — finish many at once |
| Handles branching | Very well — branch prediction, deep caches | Poorly — cores run in lockstep |
| Suits | Operating system, logic, decisions | Graphics, matrices, machine learning |
| Control logic | Large share of the chip | Small — more room for arithmetic units |
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The CPU's problem
- Code is full of decisions — if, while, function calls
- The next instruction often depends on the last result
- So it spends transistors on prediction and caching
- Making one stream fast is the whole design
The GPU's problem
- Shade a million pixels · multiply a large matrix
- Every element is independent of every other
- So thousands of simple cores can each take one
- No prediction needed — there are no branches to predict
The consequence
- A GPU is useless for sequential, branch-heavy code
- A CPU is slow at a million identical operations
- Real systems use both, for the work each suits
Independence is the test: Ask: can these operations be done in any order, at the same time, without needing each other's results?
Yes → a GPU can help. No → more cores change nothing, and the CPU's single-thread speed is what matters.
Four more differences the guide names
| CPU | GPU | |
|---|---|---|
| Design philosophy | Finish one stream of instructions as fast as possible | Push as much data through as possible at once |
| Memory access | Large caches give low latency to small, scattered pieces of data | Its own fast memory with a very wide bus: high bandwidth for big blocks of data |
| Processing power | High speed per core, few cores | Low speed per core, but a huge total for parallel arithmetic |
| Power efficiency | Better for light, sequential work and idle time | Far more calculations per watt on parallel work, but draws a lot of power at full load |
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Machine learning is the current example
- Training is dominated by matrix arithmetic, and every element is independent
- So a GPU can be orders of magnitude faster than a CPU at it
- Inference — one input, one pass — is far smaller and often runs fine on a CPU or a phone
- Which is why training needs a data centre and using the model does not
Working together: one frame of a video game
Task division
The CPU runs the game logic: input, physics decisions, what is on screen. The GPU draws the millions of pixels.
Data sharing
The CPU copies the scene data (shapes, textures, positions) into memory the GPU can read.
Coordinating execution
The CPU sends the GPU its commands and carries on; the GPU signals when the frame is ready, so the two stay in step.
CPU decides, GPU computes, they hand over data and wait for each other.
Where each is used: CPUs run operating systems, databases and game logic; GPUs run video game graphics, machine learning training and large scientific simulations such as weather forecasts.
Do not say "a GPU is faster": It is faster at parallel work. On a single sequential task a CPU core beats a GPU core comfortably, because the GPU core is deliberately simpler.
The qualification is usually the mark.
How this is tested — you must explain the difference from the SHAPE of the work, not from raw speed. It comes up two ways:
Paper 1 Section A
- Explain the differences between the CPU and the GPU, 3-4 marks
- State one task suited to each
- Give one architectural difference
Paper 1 Section B — case study
- Justify hardware for a described workload
- Explain why more cores do not always help
The classic trap: Answering "a GPU is faster". It is faster only where the work is parallel and independent. On sequential, branch-heavy code the CPU wins — and saying so is what earns the marks.
A company trains a machine learning model and also runs a database server. Explain which processor suits each, referring to how the work is structured.
Model answer plan
See the answer broken into clear points, with guidance on how each point earns marks.