Intel DAAL (the Intel Data Analytics Acceleration Library) can help to speed up big data analytics by putting forward intensely optimized algorithmic foundations for all stages of data analysis, such as pre-processing, transformation, analysis, modelling, validation, and decision-making.
Intel DAAL (the Intel Data Analytics Acceleration Library) can help to speed up big data analytics by putting forward intensely optimized algorithmic foundations for all stages of data analysis, such as pre-processing, transformation, analysis, modelling, validation, and decision-making. These function for usages including offline, streaming, and distributed analytics. It can be used primarily with the main data platforms such as Spark, Matlab, R, and Hadoop to provide super-efficient access to data. It is available for Windows, OS X, and Linux.
Intel DAAL is a highly optimized library similar to Intel MKL (the Intel Math Kernel Library). It includes computationally heavy routine that support Intel architecture such as Intel Core, Atom, Xeon, and Xeon Phi ™ processors.
Data scientists have been utilizing Intel MKL in order to solve issues with big data for a fair amount of time now. While most of Intel MKL was designed for data, all of which is to be operated upon, fits in memory at the same time, Intel DAAL handles the times when all of this data is too big to fit in memory. This is sometimes called an "out of core" algorithm. DAAL makes it so that data is available as and when it is needed, in manageable chunks, instead of all at the same time. It works for fast, effective data access on the main popular data platforms such as Dpark, Matlab, Hadoop, and R.
Intel DAAL offers built-in data management so your applications can access data from different sources directly. This includes, for example, in-memory buffer, HDFS, files, SQL database, and more.
Intel® DAAL supports three modes of processing:-
Batch processing:This is when all data fits in memory so a function processes the data at the same time.
Online processing (or streaming): This is when data does not all fit in memory, so Intel DAAL can handle chunks of data individually to combine the partial results at a later finalizing stage.
Distributed processing: Intel DAAL supports a model that is somewhat like MapReduce. Consumers in clusters process local data, while producers process, collect, and combine partial results from the consumers. DAAL gives you flexibility here, so communication functions can be left to the developers. The data movement can be chosen to work in a framework like Spark or Hadoop, or via explicitly coded communications, for example with MPI.