Parallel systems vs Distributed systems

Parallel Systems vs. Distributed Systems

Parallel systems and distributed systems are both computing architectures that involve multiple processors or computers, but they are designed to address different problems and operate under distinct principles. Parallel systems consist of multiple processors or cores within a single machine that work together to perform computations simultaneously. They aim to divide a task into smaller sub-tasks, which can be executed concurrently to improve performance and efficiency. These systems are tightly coupled, with processors sharing a common memory space, allowing for quick data exchange and coordination. Synchronization is crucial, requiring coordination among processors to ensure data consistency, often using mechanisms like locks or barriers. Parallel systems are designed to speed up processing for compute-intensive tasks, such as scientific simulations, numerical computations, and graphics rendering, presenting a single system image to the user regardless of the number of processors involved. Examples include multi-core processors in modern computers and supercomputers designed for parallel processing.

In contrast, distributed systems consist of multiple independent computers that communicate and collaborate to achieve a common goal, working over a network and allowing components to be geographically dispersed. These systems are loosely coupled, with components operating independently and communicating through message-passing over a network, which can introduce latency. Each component can function autonomously, making its own decisions and processing tasks without a central authority. Distributed systems are designed to continue operating even when some components fail, relying on redundancy and distribution of tasks for fault tolerance. They can easily scale by adding more machines without significant redesign, making them suitable for applications requiring high availability and large-scale data processing. Examples of distributed systems include cloud computing services like AWS and Google Cloud, microservices architectures, and peer-to-peer networks.

Example of Parallel systems

In summary, while parallel systems focus on the concurrent execution of tasks on multiple processors within a single machine, emphasizing synchronization and shared memory, distributed systems enable independent computers to collaborate over a network, prioritizing autonomy, fault tolerance, and scalability. Understanding these distinctions is vital for selecting the appropriate architecture for specific computing challenges.