Parallel Computing
Parallel computing is a computational paradigm that involves breaking down large problems into smaller, independent tasks that can be executed simultaneously across multiple processing units, such as CPU cores, GPUs, or distributed systems. It aims to improve performance, reduce execution time, and handle computationally intensive workloads by leveraging concurrency. This approach is fundamental in high-performance computing (HPC), scientific simulations, big data processing, and real-time applications.
Developers should learn parallel computing to tackle problems that require significant computational power, such as machine learning model training, video rendering, financial modeling, or climate simulations, where sequential processing is too slow. It is essential for optimizing applications on modern multi-core processors and distributed systems, enabling scalability and efficiency in data-intensive or time-sensitive domains. Mastery of parallel computing concepts helps in designing systems that can handle growing data volumes and complex algorithms effectively.