Massively parallel processing definition
Massively parallel processing is a method of computer processing that uses many separate processors to perform a set of coordinated computations simultaneously.
Unlike traditional computing that relies on a few processors, MPP systems include hundreds or thousands of processors, each working on a different part of a computing task. This approach is practical for large-scale data processing tasks and complex computational problems, where dividing the workload across many processors significantly speeds up processing time.
See also: parallel processing, artificial intelligence, machine learning
Usage of massively parallel processing
- Science. Scientific simulations (climate modeling, astrophysical simulations, molecular modeling, etc.) extensively use MPP.
- Big data analytics. MPP helps quickly analyze large datasets in areas of business intelligence, financial analysis, or social networks.
- Genomics and bioinformatics. Genomics requires the processing of massive datasets to understand genetic structures.
- AI. Developers use MPP to train complex machine-learning models.
- Weather forecasting. Weather prediction models and environmental simulations rely on MPP for timely and accurate forecasting.
- Financial modeling. High-frequency trading algorithms, risk management, and real-time analytics require MPP to process large volumes of financial data quickly.
- Image and signal processing. MPP greatly benefits spheres like medical imaging, video processing, and signal processing.
- Database management. Large-scale database systems and transaction processing systems in enterprise environments use MPP to handle large numbers of simultaneous transactions.
- Government and Defense. MPP provides the necessary computational power for national security, surveillance, and cryptography.
