Yiwensutong Python parallel computing: 00 basic concepts of parallel computing

Understand the concept of parallel computing in depth: In addition to understanding the basicsParallel computing concept(such as the difference between parallel and serial, the concept of data parallel and task parallelism, etc.), in-depth research should also be conducted.Parallel algorithms and parallel programming models. Learn about variousParallel architecture(such as shared memory, distributed memory and hybrid models) andParallel programming paradigm(such as message delivery, data parallelism, task parallelism, and stream parallelism).

Write and optimize parallel programs:Use Python's multiprocessing and other multiprocessing to write more complex parallel programs, such as implementing parallel sorting or parallel search algorithms. In addition, learn and practice how to optimize parallel programs, including load balancing, reducing communication overhead, avoiding synchronization delays, etc.

Performance analysis of parallel programs: Learn to analyze the performance of parallel programs using specialized tools such as Python's cProfile module or other performance analysis tools.Understand concepts such as parallel efficiency, acceleration ratio, Amdahl's law, and learn how to use these concepts to evaluate the performance of parallel programs.

Understand the challenges and solutions of parallel computing:Understand the challenges of parallel computing,Including data dependencies, race conditions, deadlocks, parallel overhead, etc., and learn how to avoid or deal with these problems.Understand the concepts of synchronization and locking in parallel programming and learn to use Python's threading or multiprocessing libraries to deal with concurrency problems.