Parallel computing involves carrying out multiple calculations or processes simultaneously, which can help solve large problems more efficiently. It has become increasingly important in computer architecture due to limitations on frequency scaling and concerns about power consumption. Parallel computing can be achieved through different forms of parallelism, and it is closely related to concurrent computing but distinct from it. The level of hardware support for parallelism can vary, and specialized architectures may be used for specific tasks. However, writing explicitly parallel algorithms can be challenging due to potential software bugs and obstacles in communication and synchronization between subtasks. The speed-up of a program through parallelization is limited by Amdahl's law.
Stanford University
Winter 2023
Focuses on efficient high-level programming through code optimization and program analysis for quality improvement. Also explores automatic memory management and natural language coding through machine learning.
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