Parallel processing has emerged as an essential approach in response to the need to handle complex tasks and voluminous datasets simultaneously. Consequently, activities that can be performed in parallel represent an evolution of operations.
This enables significant advances in the speed, efficiency, and scalability of computational operations. In this article, we will explore everything from processor architecture to the main types of parallelism. Check it out now!
What is parallel processing?
Parallel processing occurs when two or more processors interact with each other to perform a task. In other words, it involves the simultaneous execution of multiple tasks to optimize efficiency and speed up processing time.
Parallel processing works by distributing the workload among various processors, allowing different parts of the same task to be executed simultaneously. This enhances performance compared to sequential processing.
This technology can be used in various applications, but it is particularly prevalent in those that handle large volumes of data. Parallel processing involves both hardware aspects (processors, memory, and machine architecture) and software aspects (programming languages, algorithms, process mapping, and distribution).
Flynn’s taxonomy architectures
Computer scientist Michael J. Flynn created a taxonomy in 1972 to classify parallel processors. The architecture contains two streams of information: data and instructions that are used to create the four architectures of the taxonomy. Check them out:
SISD (Single Instruction, Single Data)
SISD is the simplest processor model. In this model, a single instruction unit operates on a data stream. It was seen in early single-core computers.
MISD (Multiple Instruction, Single Data)
Multiple processing units that can read multiple instructions with a single data element. In other words, multiple instructions process the same data in parallel. Examples include pipeline and data-flow architectures.
SIMD (Single Instruction, Multiple Data)
In SIMD, one instruction is applied to multiple data simultaneously. Several processing units read a single instruction for different types of accessed data in each cycle. SIMD processors used in artificial intelligence are examples of SIMD processors.
MIMD (Multiple Instruction, Multiple Data)
Multiple instructions and multiple data are read and executed simultaneously. This is the most commonly used model in modern processors, such as multicomputers and multiprocessors.
Types of parallelism
Now that we understand what parallel processing is and its architecture, let’s explore the types of parallelism. Each type focuses on different aspects and levels. Check them out:
Data parallelism
Data parallelism involves the simultaneous execution of operations, but with data division. In this case, data is processed independently and can be achieved using SIMD (Single Instruction, Multiple Data).
Task and automation parallelism
Task parallelism involves the division of instructions that must be processed concurrently. In this case, there is a dependency between the processed data, and each task may involve the execution of multiple instructions.
Instruction parallelism
Instruction parallelism is the processor’s ability to execute multiple instructions simultaneously. It can be achieved through the pipeline technique, which divides the execution of instructions into several sequential stages.
Advantages of parallel processing
Explore the main advantages of parallel processing:
Enhanced performance
One of the key advantages of parallel processing is the improvement in system performance. With this technology, multiple processors can operate in parallel to support a greater workload. Several tasks can be executed simultaneously, resulting in faster completion times for complex tasks.
Resource utilization
Parallel processing allows for more efficient handling of complex tasks and large volumes of data. Different processors or cores can execute tasks and subtasks simultaneously, enabling faster task completion and increased overall processing capacity. Correct utilization of parallel processing can lead to cost reduction as tasks complete more quickly, resulting in less hardware usage.
Fault tolerance
Another advantage of parallel processing is increased system reliability, and critical operations can be performed more securely. In parallel processing, the failure of one processor does not necessarily affect all processes, enhancing system reliability.
Parallel processing of automations with Python
The Python programming language is extremely flexible and versatile, allowing for the programming of parallel automations in various environments, systems, and infrastructures. From web applications to desktop and legacy systems, Python can efficiently create and manage parallel automations.
BotCity, an expert in Robotic Process Automation (RPA), offers an Open-Source RPA software for creating and managing code-based robots, primarily developed in Python. When choosing how automation should be executed during deployment, it is possible to connect it to more than one runner.
This means that programmed robots can execute various tasks in parallel in different environments, such as virtual machines, containers, or in the cloud. This makes the automation process of your company much more agile and efficient.
Ready to use parallel processing?
We hope this article has clarified your main questions about parallel processing. Distributing tasks across multiple processors operating simultaneously can optimize efficiency and speed up the execution of complex operations.
If you are looking for an RPA solution to automate and accelerate your company’s processes, it is worth exploring BotCity, a platform known for its flexibility in Python automations.
Feel free to schedule a conversation with our experts or, if you want to try BotCity on your own, create a free account!
