Intel® Data Analytics Acceleration Library (Intel® DAAL)

Boost machine learning and data analytics performance with this easy-to-use library

  • Helps applications deliver better predictions faster
  • Analyzes larger data sets with the same compute resources
  • Optimizes data ingestion and algorithmic compute together for the highest performance
  • Supports offline, streaming, and distributed usage models to meet a range of application needs
  • Take advantage of Priority Support―connect privately with Intel engineers for technical questions

Optimized for Your Hardware

For maximum calculation speed, each function is highly tuned to the instruction set, vector width, core count, and memory architecture of each target processor.

Intel DAAL Mlib Performance

Benchmark Source: Intel Corporation.

Benchmark results were obtained prior to implementation of recent software patches and firmware updates that are intended to address exploits referred to as "Spectre" and "Meltdown." Implementation of these updates may make these results inapplicable to your device or system.

Configuration: 2x Intel® Xeon® E5-2660 CPU @ 2.60GHz, 128 GB, Intel® DAAL 2018; Alternating Least Squares – Users=1M Products=1M Ratings=10M Factors=100 Iterations=1 MLLib time=165.9 sec DAAL time=40.5 sec Gain=4.1x; Correlation – N=1M P=2000 size=37 GB Mllib time=169.2 sec DAAL=12.9 sec Gain=13.1x; PCA – n=10M p=1000 Partitions=360 Size=75 GB Mllib=246.6 sec DAAL (seq)=17.4 sec Gain=14.2x

See below for further notes and disclaimers.1

Intel DAAL is tuned for a broad range of Intel® processors including Intel Atom®, Intel® Core™, Intel® Xeon®, and Intel® Xeon Phi™ processors targeting IoT gateways to back-end servers since applications may benefit from splitting analytics processing across several platforms.

Optimized for Developer Productivity

Provides advanced Python*, C++, and Java* data analytics functions spanning all processing stages, pre-optimized and ready to use to reduce software development time.

  • Get fast throughput with easy connections to popular analytics platforms (Hadoop* and Spark*) and data sources (SQL, non-SQL, files, in-memory)
  • Batch, streaming (online) and distributed compute models are all supported to cover a range of application data set sizes and performance requirements


Data Analysis: Characterization, Summarization, and Transformation

Low Order Moments
Computes the basic dataset characteristics such as sums, means, second order raw moments, variances, standard deviations, etc.

Computes quantiles that summarize the distribution of data across equal-sized groups as defined by quantile orders.

Correlation and Variance-Covariance Matrices
Quantifies pairwise statistical relationship between feature vectors.

Cosine Distance Matrix
Measures pairwise similarity between feature vectors using cosine distances.

Correlation Distance Matrix
Measures pairwise similarity between feature vectors using correlation distances.

Cholesky Decomposition
Decomposes a symmetric positive-definite matrix into a product of a lower triangular matrix and its transpose. This decomposition is a basic operation used in solving linear systems, non-linear optimization, Kalman filtration, etc.

QR Decomposition
Decomposes a general matrix into a product of an orthogonal matrix and an upper triangular matrix. This decomposition is used in solving linear inverse and least squares problems. It is also a fundamental operation in finding eigenvalues and eigenvectors.

Singular Value Decomposition (SVD)
Decomposes a matrix into a product of a left singular vector, singular values, and a right singular vector. It is the basis of principal component analysis (PCA), solving linear inverse problems, and data fitting.

Principal Component Analysis (PCA)
Reduces the dimensionality of data by transforming input feature vectors into a new set of principal components that are orthogonal to each other.

Partitions a dataset into clusters of similar data points. Each cluster is represented by a centroid, which is the mean of all data points in the cluster.

Finds maximum-likelihood estimate of the parameters in models. It is used for the Gaussian Mixture Model as a clustering method. It can also be used in non-linear dimensionality reduction, missing value problems, etc.

Outlier Detection
Identifies observations that are abnormally distant from other observations. An entire feature vector (multivariate) or a single feature value (univariate), can be considered in determining if the corresponding observation is an outlier.

Association Rules
Discovers a relationship between variables with certain level of confidence.

Linear and Radial Basis Function Kernel Functions
Map data onto higher-dimensional space.

Quality Metrics
Compute a set of numeric values to characterize quantitative properties of the results returned by analytical algorithms. These metrics include a confusion matrix, accuracy, precision, recall, F-score, etc.

Machine Learning: Regression, Classification, and More

Neural Networks for Deep Learning
A programming paradigm that enables a computer to learn from observational data.

Linear and Ridge Regressions
Models relationship between dependent variables and one or more explanatory variables by fitting linear equations to observed data.

Naïve Bayes Classifier
Splits observations into distinct classes by assigning labels. Naïve Bayes is a probabilistic classifier that assumes independence between features. Often used in text classification and medical diagnosis, it works well even when there are some level of dependence between features.

Builds a strong classifier from an ensemble of weighted weak classifiers, by iteratively re-weighting according to the accuracy measured for the weak classifiers. A decision stump is provided as a weak classifier. Available boosting algorithms include AdaBoost (a binary classifier), BrownBoost (a binary classifier), and LogitBoost (a multi-class classifier).

Support Vector Machine is a popular binary classifier. It computes a hyperplane that separates observed feature vectors into two classes.

Multiclass Classifier
Builds a multi-class classifier using a binary classifier such as SVM.

Alternating Linear Squares (ALS)
Collaborative filtering method of making predictions about the preferences of a user, based on preference information collected from many users..

Decision Trees
A method commonly used in data mining. The goal is to create a model that predicts the value of a target variable based on several input variables that use a decision tree as a predictive model, to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves).

Decision Forests
An ensemble learning method for classification, regression and other tasks, which operate by constructing a multitude of decision trees at training time and outputting the mode of the classes (classification), or mean prediction (regression) of the individual trees.

k-Nearest Neighbors (k-NN)
A type of instance-based learning, or lazy learning, where the function is only approximated locally and all computation is deferred until classification.


1Software and workloads used in performance tests may have been optimized for performance only on Intel® microprocessors. Performance tests, such as SYSmark and MobileMark, are measured using specific computer systems, components, software, operations, and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products. For more complete information, visit

Intel’s compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. Please refer to the applicable product User and Reference Guides for more information regarding the specific instruction sets covered by this notice. Notice revision #20110804