Data Analytics Acceleration Library

Intel Data Analytics Acceleration Library (Intel DAAL) is a library of optimized algorithmic building blocks for data analysis stages most commonly associated with solving Big Data problems.^[3][4][5][6]

The library supports Intel and compatible processors and is available for Windows, Linux and OS X operating systems. The library is designed for use popular data platforms including Hadoop, Spark, R, and Matlab.^[3][7]

History

Intel launched the Data Analytics Acceleration Library on August 25, 2015 and called it Intel Data Analytics Acceleration Library 2016 (Intel DAAL 2016).^[8] DAAL is bundled with Intel Parallel Studio XE as a commercial product. A standalone version is available commercially or freely,^[2][9] the only difference being support and maintenance related.

License

Apache License 2.0

Details

Functional categories

Intel DAAL has the following algorithms:^[3][10]

• Low Order Moments: Includes computing min, max, mean, standard deviation, variance, etc. for a dataset.
• Quantiles: splitting observations into equal-sized groups defined by quantile orders.
• Correlation matrix and variance-covariance matrix: A basic tool in understanding statistical dependence among variables. The degree of correlation indicates the tendency of one change to indicate the likely change in another.
• Correlation distance matrix: Measuring pairwise distance between items using correlation distance.
• Cosine distance matrix: Measuring pairwise distance using cosine distance.
• Data transformation through matrix decomposition: DAAL provides Cholesky, QR, and SVD decomposition algorithms.
• Principle Component Analysis (PCA): the most popular algorithm for dimensionality reduction.
• Outlier detection: Identifying observations that are abnormally distant from typical distribution of other observations.
• Association rules mining: Detecting co-occurrence patterns. Commonly known as “shopping basket mining.”
• Linear regression: The simplest regression method. Fitting a linear equation to model the relationship between dependent variables (things to be predicted) and explanatory variables (things known).
• Classification: Building a model to assign items into different labeled groups. DAAL provides multiple algorithms in this area, including Naïve Bayes classifier, Support Vector Machine, and multi-class classifiers.
• Clustering: Grouping data into unlabeled groups. This is a typical technique used in “unsupervised learning” where there is not established model to rely on. Intel DAAL provides 2 algorithms for clustering: K-Means and “EM for GMM.”

Intel DAAL supported three processing modes:

• Batch processing: When all data fits in the memory, a function is called to process the data all at once.
• Online processing (also called Streaming): when all data does not fit in memory. Intel® DAAL can process data chunks individually and combine all partial results at the finalizing stage.
• Distributed processing: DAAL supports a model similar to MapReduce. Consumers in a cluster process local data (map stage), and then the Producer process collects and combines partial results from Consumers (reduce stage). Intel DAAL offers flexibility in this mode by leaving the communication functions completely to the developer. Developers can choose to use the data movement in a framework such as Hadoop or Spark, or explicitly coding communications most likely with MPI.

References

External links

• daal on GitHub
• DAAL Official Product Website
• DAAL Support
• DAAL User Forum
• DAAL Support Channel