Data Parallelism

Data Parallelism is a technique used in distributed computing and parallel computing to divide a large dataset into smaller subsets and process them simultaneously on multiple processors or nodes. In this technique, each processor or node receives a copy of the same program and operates on a different subset of the data. The results are then combined to produce the final output. Data Parallelism is commonly used in machine learning and deep learning applications, where large datasets need to be processed in parallel to train complex models. It can significantly reduce the training time and improve the scalability of the system. However, it requires careful design and synchronization to ensure that the results are consistent and accurate.
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