112-types-of-cpu

Question 1

instruction set

Answer 1

• set of all possible instructions a processor can recognise and execute. Made available by hardware design of processor
  -instructions are machine code that the processor has been designed to handle based on a specific processor instruction set
• Assembly (programming language) lets you combine these instructions to make a program.
  -included in ISA

Question 2

ISA (instruction set architecture)

Answer 2

• processor architecture specification
• standardizes how software can interact with a computer’s hardware
• including necessary details for software developers to write programs.

Question 3

CISC (complex instruction set computer)

Answer 3

• Complex architecture design with many specialized instructions.
• Designed to execute tasks in as few lines of assembly code as possible

Question 4

CISC good points

Answer 4

• Assembly code is relatively short, less RAM to store instructions -> easier to translate to machine code.
  -can execute series of operations in one instruction
• Offers consistency and backwards compatibility with both software and hardware.
• Has a single register set with variable-length instructions and addressing modes.

Question 5

CISC bad points

Answer 5

• Instructions may take multiple cycles to execute, making pipelining more difficult.
• Low-level operations may be carried out as part of a single instruction, making software run more slowly - processor has to carry out each operation sequentially.
• Complexity of the hardware can be expensive to manufacture, generate more heat, and require efficient cooling features.
• Some instructions may rarely get used.

Question 6

RISC (reduced instruction set computer)

Answer 6

• Uses simple instructions executed within a single clock cycle for fast execution and enabling pipelining.
• Has a small instruction set (with a limited amount of instructions)
• standardized, fixed-length instructions that run faster and are used regularly in multiple general-purpose registers.

Question 7

RISC good points

Answer 7

• Simple circuitry and processor design
• minimize manufacturing costs, generate less heat, fewer transistors and consume less power,
• leaving more space for general-purpose registers and cache.
• Suitable for low-power, portable, or battery-powered devices that require high-speed parallel processing, such as smartphones, tablets, smartwatches, printers, home assistants, and TVs.

Question 8

RISC bad points

Answer 8

• Complex tasks in RISC may require multiple instructions, leading to longer programs
• more complex compiler as needs to do more work to convert due to the need for more assembly mnemonics,
  -and increased RAM usage for storing instructions.

Question 9

CISC and RISC with pipelining

Answer 9

• CISC more complex, harder to divide instruction processing into smaller stages. And has variable lengths, pipeline stalls/flushes.
  -risc simple and streamlined, easier to break into smaller stages

Question 10

co-processor

Answer 10

• Co-processors are additional processors used for specialized tasks.
• improve the overall speed of a computer by executing concurrently with the main CPU and reducing its workload.
• used for complex functions, such as calculating floating point numbers.
  -The main CPU offloads some of its tasks to the co-processor, which then executes them in parallel.

Question 11

GPUs

Answer 11

specialised co-processor designed to handle and manipulate large amounts of data, particularly for graphics-related tasks and other specialized tasks.

can be integrated into a computer’s motherboard or used as a dedicated graphics card

Question 12

GPU pros

Answer 12

• Many cores - optimised for parallel processing, allows them to process similar data simultaneously: image processing, video transcoding, cryptocurrency mining
• Designed to handle and display high-quality graphics - graphic processing, gaming, virtual and AR, digital content creation: 3D modelling and animation, medicinal imaging and analysis
• High processing power - handles large amounts of data and complex numerical calculations: scientific simulations and modelling, financial modelling and analysis, AI and neural networks

Question 13

GPU cons

Answer 13

• expensive, consume a lot of power, more complex to program and use
• typically have less memory than a CPU, limit their ability to handle certain tasks.
• GPUs can be large and require a dedicated slot in a computer case, which can limit the flexibility of a computer’s configuration.

Question 14

Difference between GPUs and CPUs

Answer 14

• CPUs general purpose processors, GPUs specialized for certain tasks e.g graphics processing.
• GPUs faster and more efficient for simple operations on large data sets, CPUs at complex operations on small data sets.
• GPUs optimized for parallel processing calculations, CPUs process tasks in a serial order.
• GPUs thousands of cores, CPUs typically smaller, limited amount of cores.

Question 15

Why the need for multicore and parallel systems?

Answer 15

• software complexity/demands increase = more instructions needed for processing (time-consuming)
• Multicore/parallel systems allow multiple processors to share and perform tasks simultaneously
• reducing completion time and improving performance.

Question 16

Parallel processing

Answer 16

• Dividing tasks/program instructions into smaller sub-tasks that can be executed simultaneously by distributing across multiple processors/cores.
• Achieved through pipelining or threading and can use multiple processors, multiple cores on a single processor, or a combination of both.
  -Technique to achieve faster processing of large amounts of data or complex tasks

Question 17

Multicore processor

Answer 17

• multiple independent processing units on one processor
• suitable for large projects, multitasking by splitting processing workload across
• Manufacturers integrate cores onto a single physical chip to optimize performance.
• used in modern computers, servers, smartphones, and other electronic devices.

Question 18

Concurrent processing:

Answer 18

• completing multiple tasks or processes at given time intervals by giving them processor time slices - looks as if simultaneously (multitasking)
• may not necessarily require multiple processors/cores or specialized hardware and not necessarily splitting up tasks.
• several processes work simultaneously to solve a problem
  -multiple processors carrying out different processes
  -bottlenecks like accessing storage device, time complexity, binary search cannot be performed concurrently
• Common techniques for concurrent processing include multi-threading, multi-processing, pipelining, and distributed computing.

Question 19

Differences of parallel processing and multicore system

Answer 19

• multicore systems have multiple independent cores that complete separate fetch-execute cycles, while parallel systems complete multiple instructions simultaneously using techniques like pipelining and threading.
• Both multicore systems and parallel systems can achieve parallel processing, but multicore systems use multiple cores on a single processor chip, while parallel systems can use multiple processors or processing cores across multiple chips.

Question 20

Similarities of parallel processing and multicore system

Answer 20

• more than one processor working together
• single job is split into tasks and delegated to the best processor/core for that task based on workload, available resources, and core/processor capabilities.
• job is completed more quickly
• controlled by a single operating system.

Question 21

Supercomputer

Answer 21

• Supercomputer: high-performance (speed and processing) computing system, designed to process large and complex computational tasks quickly
• Used for scientific and engineering simulations, weather forecasting, financial modelling, and other computationally intensive applications
• Companies book supercomputers for data processing and scientific/engineering applications

Question 22

Supercomputer features

Answer 22

• Parallel processing, greatly increasing the speed at which problem is solved.
• specialized architectures and operating systems optimized for high speed complex computations of specific tasks
• Uses thousands of CPU and GPUs to process data quickly

Question 23

Limitations of parallel processing

Answer 23

-an algorithm must be suitable for it
- unequal process sizes means not all processors are always utilised
-increased complexity of CU means increased time spent managing processes instead of executing them
- potential for deadlock where one process requires the output of another