2-2-2. Hybrid Parallel Processing

Data parallelism and model parallelism in AI development are important technologies for efficiently processing large data sets and complex models. Data parallelism is a method of dividing a large dataset into multiple parts and processing each part concurrently. Each processing unit (CPU, GPU, or machine) processes a subset of the data independently and aggregates the results. Model parallelism is a method of dividing a large and complex model into multiple parts and processing each part concurrently on different processing units. Each processing unit is responsible for a part of the model and advances calculations while communicating intermediate results with other processing units.

EMETH has developed hybrid parallel processing that combines these two. The feature of hybrid parallel processing is that it performs processing simultaneously on multiple nodes, which significantly reduces the computation time of machine learning from large-scale image classification tasks to natural language processing, and can perform more advanced functions.

To implement hybrid parallel processing, you need hardware and software frameworks that support parallel processing. Also, the ability to achieve proper division of data and models, efficient communication and synchronization, and load distribution, is a feature that could only be achieved by the EMETH team with specialized knowledge and experience in AI development.