What is Parallel Processing?

The process of running two or more processors together is known as parallel processing. In parallel processing, a single process is divided into multiple parts, and each individual part is executed at the same time. It is used to handle various parts of a task at the same time. The main advantage of parallel processing is the reduction of the processing time of the program, where the processor in a system is the CPU. Hence, parallel processing can be performed by using multiple CPUs, and having more than one CPU creates a multi-processor. Another name of parallel processors is block processors. Here, the size of a block is the number of inputs that can be processed simultaneously in a single clock cycle.

Multi-core processors

A system with more than one processor is known as a multi-processor. It is also known as a parallel system, which contains integrated circuit chips. Computers usually have 4 core processors and the number can extend up to 12 processors in a single system. There are two broad classifications of multi-processors.

  • Symmetric processor- In this type, the multiple processors contain the same type of operating system. This kind of relationship is known as the peer-to-peer relationship between processors. Each processor, which is in a symmetry system is capable to manage the computing of heavy data. After that, the number of processors in a symmetric system increases along with the data propagation time. The Encore version of UNIX has symmetric processors.
  • Asymmetric processor- They have a master processor that gives instructions and tasks to the underlying processors to perform a predefined task. This type of relationship is known as a master-slave relationship. It was invented before the symmetric processors.

SMP and MMP are further classifications of multi-core processors. SMP represents the symmetric system, whereas MMP represents massive parallel processing systems.

A step-by-step description of parallel-processing

  1. The complex task to be performed is divided into different parts with the help of software tools.
  2.  Each of these parts is assigned to individual processors.
  3. The final output from each processor is combined using another software tool. Communication between individual processors occurs using software and the data exchange is in sync with each other.
  4. The final software collects individual computing outputs from these processors and the pieces are fit together only with sync between processors.

It is commonly used in SAP servers to process programs in the background.

Types of parallel processing

There are four types of parallel processing architectures namely Single Instruction Single Data (SISD), Single Instruction Multiple Data (SIMD), Multiple Instruction Single Data (MISD) and Multiple Instruction Multiple Data (MIMD) respectively. The most commonly used types of parallel processing are SIMD and MIMD. Single instruction multiple data (SMID) is a type of parallel processing. In this type, the processors handle different types of data but perform the same set of the instruction streams. It is used by data scientists for data analysis. It can easily do the computing of large data sets that have a specified benchmark. Multi instruction multiple data (MIMD) is also a common form of parallel processing. In this type of processing, the data handled by the processors and their instruction set are different from each other. The other type of multiple instruction, single data (MISD) involves the same input data used with different algorithms for each processor. Instruction-level parallelism is the type used to process low-level machine instruction applications.

Parallel processing architecture types

SIMD or Single Instruction Multiple Data

It is an integral part of the hardware design of our system. It is used to simultaneously process the same computing operation on different data sets. It can be directly accessed using an instruction set known as an ISA. Parallel computation is used for data computing and to divide the data to each of the processors. It is mainly used to enhance the efficiency of multimedia use for modern CPU designs. It can be divided into three subcategories. This division is based on Flynn's 1972 taxonomy. An important subdivision of SIMD is SIMT.

It was first used in 1966 for ILLIAC IV. The supercomputers which were vector types used the base of SIMD for their functional processors. This type of parallel processing has undergone various developments throughout all these years and now its version supports real-time graphics. A programming model like this can solve parallel problems with high speed and efficiency.

The main advantage of single instruction multiple data processors is in multimedia operations. In situations such as adding a particular value to a large block of data can be easily done using the SIMD. It is possible to load all data in a single step by using this type of processor. Eight values of data can be added at a single operation.

This type of processor has its disadvantages. The main disadvantage is that the vectorization of the algorithms is very difficult in this type of processor. The size of the file that needs to undergo computing affects the consumption of power and the area of the chip. So, to use larger register files, larger chips and higher power are needed.

MIMD or Multiple Instruction Multiple Data

The architecture of Multiple Instruction Multiple Data type processors has a parallel structure. Individual processors are tightly coupled with each other and all of them share a common memory. This shared memory is unique to these individual processors and other processors cannot access it. The operation of this type of architecture can be considered asynchronous. Each processor works independently to process different pieces of data.

MIMD can be classified into two different types.

  • Shared-memory MIMD- A group made of memory modules and processors is created in this type of architecture. Interconnection networks are used to access these memory modules. The universal address space is represented by these modules that are shared by the processors. This type of computer architecture is easy to program because of its global memory space.
  • Distributed memory MIMD- In this type of architecture, the clones of memories are created and each of these clones is known as a processing element.

It is an interconnection network that is used to link the processing elements. Each processor is considered to have its memory, and accessing the memory of another processor is prohibited. If an individual processor needs any data from the remote processor memory, a signal is sent to the remote processor as a request for the required data. As the distance between two processors increases, the access time of data also increases.

Advantages of parallel processing

Parallel processing helps to improve performance. It can help to save power consumption and it is an efficient way to perform multiple tasks at the same time. It is mainly used for data processing and other complex computations. They reduce the processing time, making it easier and faster to perform lengthy complex programs. Real-time parallel processing is a key part of machine learning. Parallel computers are widely being used by data scientists for the analysis of a large set of data. Parallel computers use shared memory which acts as a global address space between each processor. Machine learning can be easily related to artificial intelligence. When machine learning is done on a single processor, a substantial delay can be observed. This can compromise the efficiency of the machine. Parallelizing common tasks can boost the performance of machine learning algorithms.

Context and Applications

The topics of network design are taught in many courses such as:

  • Bachelors in Computer Science
  • Masters in Computer Science
  • Bachelors in Computer Application
  • Masters in Computer Application

Practice Problems

1. What is the main advantage of parallel processing?

  1. Readability of code
  2. Efficiency
  3. Reduction of run-time
  4. Fewer errors

Answer- Option c

Explanation- This is because parallel processing reduces rum time.

2. What is a group of instructions organized in a sequence as executed by the machine known as?

  1. Instruction portfolio
  2. Instruction stream
  3. Shared-memory
  4. Parallel programs

Answer- Option b

Explanation- A group of instructions is known as an instruction stream.

3. What are the main use of parallel computers?

  1. Modeling structures
  2. Instruction-level programming
  3. Object-oriented programming
  4. Machine language applications

Answer- Option a

Explanation- Parallel computers are used for modeling.

4. Which of the following represent single symbol of data in computing?

  1. Data block
  2. Data bit
  3. Datum
  4. Bit

Answer- Option c

Explanation- A single symbol of data is known as a datum

5. Which of the following has master-slave relation?

  1. SIMD
  2. MIMD

Answer-Option d

Explanation- Asymmetric processors have master-slave relations.

Common Mistakes

Parallel processing is also a prominent topic in psychology. Data processing in the brain can be compared to parallel processing in computers. Different types of information and senses are stimulated simultaneously in the brain. A similar topic title can confuse students.

Another mistake made by programmers includes inconsistency in querying of data. The number of processors should be consistent with the size of the data. Mistakes made in communication between servers will lead to improper processing of data and loss of data.

  • Processors
  • Dorsal and ventral pathways
  • Parallel manipulators
  • Parallel algorithm
  • Multimodal signalling
  • NUMA memory architecture

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