With technology advancing at an accelerated pace, the need to execute complex tasks quickly is becoming increasingly evident. In this context, parallel programming stands out for enabling companies to achieve new levels of performance and scalability.
Moreover, the growing demand for analyzing large datasets, training artificial intelligence models, and automating processes underscores the relevance of this approach in today’s landscape.
In this article, we’ll explore what parallel programming is, how it works, and why it is crucial for the development of intelligent automation. Want to learn more? Keep reading!
What is parallel programming?
Parallel programming is a development technique that allows multiple tasks to be executed simultaneously to maximize the use of available hardware resources, such as multicore processors and GPUs.
In other words, instead of performing operations sequentially, parallel programs divide tasks into smaller parts that can be processed at the same time, boosting performance.
Imagine following a cake recipe: while one person mixes the ingredients, another preheats the oven, and a third prepares the frosting. By performing these actions simultaneously, the cake is ready faster. This is exactly what parallel programming does in the computational world.
How does parallel programming work?
Parallel execution is based on three main pillars:
- Task division: Breaking the code into blocks that can be processed simultaneously.
- Simultaneous execution: Allocating these parts to different cores or processing units.
- Synchronization: Combining the results of independent parts to form the final solution.
What is the importance of parallel programming?
Parallel programming allows developers to create faster, scalable applications capable of handling complex tasks. Here are its key advantages:
Improved performance
The main advantage of parallel programming is its ability to speed up processing by dividing tasks into smaller parts and executing them simultaneously. This significantly reduces the time required to complete complex operations.
Scalability
Parallel programming also offers scalability. As technologies evolve, modern systems rely on multicore architectures and distributed computing, which require advanced techniques to fully utilize these resources.
Learn more: What is RPA automation architecture?
Parallel programming models
As we’ve seen, parallel programming is used to optimize processing across various applications. Here are the main models to implement parallelism:
Threads
Thread-based parallelism uses threads—smaller execution units within a program—to perform tasks simultaneously. In this approach, a program is divided into multiple threads, each handling part of the processing.
For instance, in an RPA automation system, threads can execute multiple tasks simultaneously, such as data collection and report generation, improving process efficiency.
Learn more: What is automation software and its benefits?
Data parallelism
This model divides a large dataset into smaller parts, which are processed simultaneously. Each processing unit independently handles a subset of the data.
For example, during sales data analysis, the dataset can be split by geographic region. While one core processes data from South America, another analyzes Europe, delivering faster results.
GPUs and high-performance computing
GPUs (graphics processing units) execute thousands of parallel tasks, making them ideal for intensive applications. Unlike CPUs, which have fewer cores optimized for sequential tasks, GPUs feature hundreds or thousands of smaller cores designed for parallel calculations.
For example, in training AI models, GPUs process large data batches simultaneously, accelerating machine learning from days to hours—or even minutes.
Parallel programming and RPA
Parallel programming is a vital tool for advancing robotic process automation (RPA). This approach enables the execution of multiple tasks simultaneously.
This capability is especially valuable in scenarios where resource optimization is essential, such as updating RPA bots on remote machines or running multiple headless runners on a single virtual machine (VM).
For instance, BotCity offers solutions that support the execution of multiple automations in parallel, optimizing resource usage and enhancing operational efficiency.
Automated RPA updates
Parallel programming enables simultaneous updates of RPA bots across multiple remote machines.
Instead of performing updates sequentially, where each machine is configured individually, parallel execution breaks the task into blocks, allowing multiple instances to be updated at the same time.
For example, when implementing bot updates on virtual machines, parallel programming facilitates:
- Process Acceleration: Updates are performed across multiple machines simultaneously, reducing downtime.
- Centralized Control: Platforms like BotCity Orchestrator leverage parallelism to manage updates, ensuring all remote machines are synchronized and running the same version.
Learn more: Containers vs. VM: pros, cons, and orchestration techniques.
Parallel execution of RPA automations
Another practical example is the simultaneous execution of multiple RPA bots (headless runners) on a single virtual machine. Thanks to parallel programming, a single VM can divide its processing capacity to run different automations simultaneously without interference.
This offers benefits such as:
- Resource Optimization: Running multiple automations on the same VM reduces infrastructure costs and maximizes hardware usage.
- Increased Productivity: While one bot performs administrative tasks, another can process data or handle real-time interactions, all happening in parallel.
Learn more: What is parallel processing and its advantages.
Parallel programming and automation with BotCity
Parallel programming and intelligent automation work hand in hand to achieve greater efficiency and scalability. At BotCity, these concepts are implemented through robust tools that enable the simultaneous execution of multiple processes and workflows.
One of BotCity’s standout features is the Orchestrator, a solution designed for centralized and efficient automation management. Based on the principles of parallel programming, the orchestrator allows multiple bots to execute tasks simultaneously.
The BotCity Orchestrator centralizes automation management, schedules tasks, monitors processes in real time, and efficiently distributes workloads among various bots, maximizing process efficiency. It organizes and assigns tasks across bots in parallel, ensuring each process is executed in the correct order and priority.
This functionality is especially useful in high-demand scenarios, such as processing large volumes of data.
Learn more: What is orchestration in IT, its types, and tools?
The importance of parallel programming
As discussed throughout this article, parallel programming is the foundation for scalable and efficient automations. With BotCity, your business can leverage the full potential of parallel programming by implementing intelligent automations that boost productivity and reduce operational costs.
Whether managing bots simultaneously or integrating large-scale automations, BotCity offers solutions that streamline processes and enhance productivity.
Ready to take the next step in intelligent automation? Talk to one of our specialists today or create a free account to get started!
