Computation modes refer to how the functions in the library interact with the data that the data management part of the library handled. This can be divided into 3 types of interactions, batching, online and distributed. While batching and distributing are just efficient code handling of large data sets, distributed processing falls under parallel processing. One of the reasons why the computation is quick is simply because the data being accessed has been efficiently organized by the data management part of the library. Additionally, many functions in the library have the potential to be used by two or even three different computation modes. This gives the developer a lot of freedom in deciding how to handle the data. If the data set is not so large, it may be much better to do batch processing. Whereas if the data set is actually very large, it may make a lot more sense to use distributed processing.

The majority of Many common functions in the library appears appear to be simple batch processing. I believe batch processing is the equivalent to serial code, where the algorithm works with the entire block of data at once. However, the library is still quite optimized even in these situations. For example the sort function:

We can see that the DAAL version versus a vector quick sort is much slower with a small data collection but as the data set gets larger and larger it starts to outperform the quick sort more and more. This shows that there is a small amount of overhead when calling this function and to only use it for larger data sets.