Data Parallelism

Data parallelism is a type of parallel computing that involves running the same task on multiple sets of data at once. It splits data into subsets, typically referred to as ‘chunks’, which are then processed in parallel by multiple compute cores or nodes. As each core works on its own subset of data simultaneously, the result can be computed much faster than when using only one core or node. This makes it ideal for tasks where large datasets need to be processed quickly and efficiently. Data parallelism is particularly useful for applications such as machine learning and deep learning, where it can speed up the training process significantly.  

It is distinct from other forms of parallel computing such as task parallelism, which involves running different tasks in parallel on the same data. By contrast, data parallelism splits the same task into multiple parts that are all run independently at the same time.  

Data parallelism is an example of distributed computing and allows for a much more efficient use of computational resources than would be possible with only one core or node. As such, it has become increasingly popular in recent years due to its ability to scale up applications quickly, resulting in increased performance and cost savings.  

Overall, data parallelism is an incredibly powerful tool for speeding up computation times and making better use of hardware resources by running workloads simultaneously instead of sequentially. It can provide significant performance improvements over traditional computing architectures, particularly when dealing with large datasets or complex tasks.

The CS-2 supports data-parallel execution at smaller neural network batch sizes than clusters of traditional accelerators, and it also enables more flexible layer-pipelined execution modes.