Graphics Processing Unit (GPU) (OCR A Level Computer Science)
Revision Note
Author
Jamie WoodExpertise
Maths
Graphics Processing Unit (GPU)
What is a GPU?
A graphics processing unit (GPU) is responsible for processing graphics within the computer to reduce the load on the CPU
CPUs are general purpose processors whereas GPUs are designed specifically for graphics
GPUs are likely to have built in circuitry or instructions for common graphics operations
GPUs can perform an instruction on multiple pieces of data at one time
This is useful when processing graphics (e.g. transforming points in a polygon or shading pixels) which means it can perform transformations to on screen graphics quicker than a CPU
The GPU can either be part of the graphics card or embedded in the CPU
A GPU will usually be multicore and can have up to 76 cores
What can a GPU be used for besides graphics?
Besides graphics processing, a GPU can also be used for:
3D modelling
The GPU can be used to render lighting effects, textures and shadows
Data modelling
As GPUs can handle many calculations simultaneously, they can handle large datasets and complex operations like sorting and filtering data
Financial modelling
GPUs are used to simulate different scenarios in risk modelling, option pricing and other financial modelling types
Lots of simulations can be run in parallel
Data Mining
Data mining is the process of analysing large amounts of data to find patterns
The main computational tasks are sorting, searching, pattern recognition, statistical analysis and graph algorithms
Performing Complex Numerical Calculations
Matrix multiplication and inversion can be done in parallel
Numerical Simulations
Physics and engineering simulations often involve solving complex maths models, which can be done in parallel
Solving Differential equations
Solving differential equations involves computations which can be performed in parallel
Machine learning
This involves training a computer on a massive amount of data which can be done in parallel. There are lots of matrix multiplications and other computations which can be performed
After the training, GPUs can be used to speed up the process of making predictions on new data
Calculations on multiple data at the same time
There are a number of scenarios where calculations will be needed to be carried out on multiple data at the same time e.g. insurance pricing, modelling risk, calculating bills
This is done by GPUs rather than CPUs due to being set up for parallel processing
What types of task are GPUs suited for?
GPUs are suited to certain tasks that utilise:
Specialist instructions
GPUs are designed to execute specialist instructions which are common in 3D graphics rendering such as operations on matrices, vectors and geometric transformations
These capabilities have been expanded over time and have been generalised which makes GPUs suitable for a wide range of complex calculations besides graphics processing
Multiple cores
Although a CPU can have multiple cores, these are optimised for serial processing
GPUs have smaller cores but these are optimised for parallel processing
GPUs can perform many calculations simultaneously - ideal for tasks that can be broken down into smaller parts
This is useful in machine learning and situations where large amounts of data need to be processed
SIMD processing
Single Instruction Multiple Data (SIMD) processing is computers that have multiple processing elements which perform the same operation on multiple data points simultaneously
GPUs support SIMD processing as they were originally designed to perform the same operations on multiple pixels or vertices simultaneously - this is a common requirement in image processing, simulations and machine learning
Exam Tip
You don’t need to know the ins and outs of these uses of GPUs (like how to solve a differential equation) but you need to know what GPUs can be used for besides graphical processing
What are the benefits of using a GPU?
There are a number of benefits to using a GPU as well as a CPU (it isn’t possible to only use a GPU as the CPU assigns tasks to the GPU)
GPUs can handle many tasks simultaneously as they are multicore processors
Speed
As GPUs can use parallel processing, this speeds up tasks, particularly those involving large amounts of data or complex computations
Efficiency
GPUs can perform more calculations per unit of power consumed in comparison to CPUs making them more energy efficient when it comes to parallel tasks
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