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Massively parallel processing

Massively parallel processing

(also MPP)

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.

Further reading

Ultimate digital security