What is parallel computing and how is it used in data science?

What is parallel computing and how is it used in data science?

Parallel computing is a type of computation where many calculations or processes are carried out simultaneously. In data science, it's used to process large datasets more efficiently by dividing tasks across multiple processors.

Can you give me an example of how it's applied in machine learning?

Sure! In machine learning, parallel computing can be used to train models on large datasets by splitting the data into smaller chunks and processing them concurrently, which speeds up the training time significantly.

What are some popular frameworks for parallel computing?

Some popular frameworks include Apache Spark, Dask, and TensorFlow, which all support parallel processing to enhance performance.

Are there any challenges associated with parallel computing?

Yes, challenges include managing data dependencies, ensuring load balancing, and dealing with potential race conditions.