Intel Data Analytics Acceleration Library is essentially a library which is optimized to work with large data sources and analytics. It covers the comprehensive range of tasks that arise when working with large data, from preprocessing, transformation, analysis, modeling, validation and decision making. This makes it quite flexible as it can be used in many end-to-end analytics frameworks. What this means is that we can use this library to extract data from files, store files, structure the data in the files in an orderly way and perform complex operations on that data - all within the same library.

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 online types are just efficient code handling of large data sets, distributed processing falls under what we know as 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 final method of processing in the library is distributed processing. This is exactly what it sounds like, the library now forks different chunks of data to different compute nodes before finally rejoining all the data in one place.These functions are obviously best used for larger data sets and more complex operations.