EnglandComputer ScienceSyllabus dot point

What makes one processor faster than another, and when is a GPU or a multi-core design the right choice?

Factors affecting processor performance (clock speed, number of cores, cache size and type), pipelining, and the characteristics and uses of CISC and RISC processors, multicore and parallel systems, and GPUs.

An OCR H446 answer on what affects processor performance: clock speed, cores and cache, how pipelining overlaps the fetch-decode-execute stages, and the characteristics and uses of CISC versus RISC processors, multicore and parallel systems, and GPUs.

Generated by Claude Opus 4.813 min answerUpdated 2026-06-02

Reviewed by: AI editorial process; not yet individually human-reviewed

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Quick answer

Clock speed (in $\text{GHz}$ ) sets how many cycles per second the processor runs, so a higher clock generally executes more instructions per second. More cores let the processor run several instructions truly simultaneously, useful for multitasking and parallelisable work. Larger and faster cache (L1, L2, L3) reduces how often the slow main memory must be accessed, cutting average access time. Pipelining overlaps the fetch, decode and execute stages of consecutive instructions to raise throughput. CISC chips have many complex instructions (fewer instructions per program, harder to pipeline); RISC chips have few simple fixed-length instructions (more instructions per program, easy to pipeline, low power). GPUs have thousands of simple cores ideal for data-parallel work such as graphics and machine learning, while multicore CPUs suit a few varied threads.

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What this dot point is asking

OCR wants the three classic factors that affect a single processor's performance (clock speed, cores, cache), how pipelining squeezes more throughput from the fetch-decode-execute cycle, and the characteristics and typical uses of CISC and RISC processors, multicore and parallel systems, and GPUs. Expect "explain" and "discuss" questions comparing designs against a workload.

The answer

Factors affecting performance

Key fact

Clock speed is the frequency of the processor clock (for example $3.2\ \text{GHz} = 3.2 \times 10^{9}$ cycles per second); each cycle can advance the fetch-decode-execute cycle, so a higher clock usually means more instructions per second, but heat and power limit how high it can go. Number of cores: each core is an independent processing unit, so a quad-core chip can execute four instruction streams at once; the gain only materialises if the software is written to use multiple threads. Cache is a small, very fast memory between the CPU and main memory holding recently and frequently used data and instructions; a cache hit avoids a slow main-memory access. L1 is smallest and fastest (on the core), L2 is larger and slightly slower, L3 is largest and shared between cores.

Pipelining

CISC versus RISC

Parallel systems and GPUs

Compare two processors for a workload

A processor A runs at $4.0\ \text{GHz}$ with 2 cores; processor B runs at $2.5\ \text{GHz}$ with 8 cores. A task can be split into 8 fully independent threads. Which processor is likely faster, and why?

step 1: Identify the workload type

The task splits into 8 independent threads, so it is highly parallelisable; throughput depends on how many threads can run at once, not just raw clock speed.

step 2: Compare core counts against the threads

Processor B has 8 cores, enough to run all 8 threads simultaneously. Processor A has only 2 cores, so it must time-share 8 threads across 2 cores, processing roughly four at a time.

step 3: Weigh clock speed against parallelism

A's higher clock ( $4.0$ vs $2.5\ \text{GHz}$ ) helps each individual thread, but B can run four times as many threads concurrently. For a fully parallel task the core count dominates.

step 4: Conclude

Processor B is likely faster for this workload because it executes all eight threads at once; A would be faster only for a single-threaded task where its higher clock wins.

Examples in context

A laptop CPU might pair four fast cores with 8 MB of shared L3 cache for responsive general use, while its integrated GPU handles parallel graphics work. Smartphones use ARM RISC processors because low power extends battery life. Supercomputers and AI training rigs stack thousands of GPU cores to exploit data parallelism. Pipelining is why modern CPUs complete close to one instruction per cycle on straight-line code, and why branch prediction exists, to avoid flushing the pipeline on every jump.

Try this

Q1. State two factors, other than clock speed, that affect the performance of a single processor. [2 marks]

Cue. Number of cores and cache size or type (also pipelining, word length, bus width).

Q2. Explain why RISC processors are common in mobile devices. [2 marks]

Cue. Simple fixed-length instructions pipeline efficiently and the simpler hardware uses less power, extending battery life.

Q3. State what type of task benefits most from a GPU. [1 mark]

Cue. Highly data-parallel tasks where the same operation is applied to many data items (for example graphics, matrix arithmetic, machine learning).

Exam-style practice questions

Practice questions written in the style of OCR exam questions on this dot point, with worked answer explainers. The year tag is the paper they imitate, not the source.

OCR 20186 marksA games developer is choosing hardware to train a machine-learning model that processes large matrices. Discuss whether a multi-core CPU or a GPU is better suited to this task.

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This is a levels-of-response question; reward a developed comparison reaching a judgement.

A GPU has thousands of simple cores designed for the same operation applied across many data items at once (data parallelism / SIMD), which matches matrix and vector arithmetic where each element is processed identically. A multi-core CPU has a few powerful, general-purpose cores with large caches and complex control, better for varied, branch-heavy tasks and a small number of threads.

Matrix-heavy machine learning is highly parallel and uniform, so the GPU's many cores give far higher throughput; the CPU would underuse its few cores on such regular work. Conclusion: the GPU is better here, though the CPU still coordinates the work and handles non-parallel parts. Top marks need the data-parallel argument explicitly linked to the matrix workload.

OCR 20224 marksExplain how pipelining improves processor performance, and state one situation in which the benefit is reduced.

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Pipelining (3 marks): the fetch, decode and execute stages of different instructions are overlapped so that while one instruction is being executed, the next is being decoded and a third fetched. This keeps each part of the processor busy and increases instruction throughput (more instructions completed per unit time) without raising the clock speed.

Reduced benefit (1 mark): a branch or jump instruction can mean the instructions already fetched are wrong and must be discarded (a pipeline flush/stall), or a later instruction depends on an earlier result (a data hazard). Markers reward "overlapping stages to increase throughput" plus a valid hazard.

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Sources & how we know this

OCR AS and A Level Computer Science (H046, H446) specification — OCR (2015)