Intel® oneAPI Data Analytics Library

Deploy High-Performance Data Science on CPUs and GPUs

Maximum Calculation Performance

  • Provides the right tools to build compute-intense applications that run fast on Intel® architecture
  • Optimized for CPUs and GPUs

High-Speed Algorithms

  • Includes algorithms for analysis functions, math functions, and training and library prediction functions for C++ and Java
  • Used to optimize algorithms from popular machine-learning Python libraries, such as XGBoost and scikit-learn* (made available as part of the Intel® AI Analytics Toolkit)

What You Can Do

  • Analyze large datasets with available compute resources.
  • Make better predictions faster.
  • Optimize data ingestion and algorithmic compute simultaneously.
  • Support offline, streaming, and distributed usage models.

Intel® oneAPI Data Analytics Library is available as part of the Intel® oneAPI Base Toolkit.

Explore the Intel® oneAPI Base Toolkit

Develop in the Cloud

Get what you need to build, test, and optimize your oneAPI projects for free. With an Intel® DevCloud account, you get 120 days of access to the latest Intel® hardware—CPUs, GPUs, FPGAs—and Intel oneAPI tools and frameworks. No software downloads. No configuration steps. No installations.

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Download the Library

oneDAL is included as part of the Intel® oneAPI Base Toolkit.

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Features

Performance and Portability

  • To ensure maximum calculation speed, each function is highly tuned to the instruction set, vector width, core count, and memory architecture of each target CPU or GPU.
  • See performance benefits for a wide range of applications—from IoT gateways to back-end servers.
  • Work in the language you are most familiar with and get maximum performance in your application with integrated Python*, Java*, C, and C++ support.

In-Depth Algorithm Support

Supported Algorithms:

  • Apriori for Association Rules Mining
  • Correlation and Variance-Covariance Matrices
  • Decision Forest for Classification and Regression
  • Expectation-Maximization Using a Gaussian Mixture Model (EM-GMM)
  • Gradient Boosted Trees (GBT) for Classification and Regression
  • Alternating Least Squares (ALS) for Collaborative Filtering
  • Multinomial Naïve Bayes Classifier
  • Multiclass Classification Using a One-Against-One Strategy
  • Limited-Memory BFGS (L-BFGS) Optimization Solver
  • Logistic Regression with L1 and L2 regularization support
  • Limited-Memory BFGS (L-BFGS) Optimization Solver
  • Linear Regression

 

Supported CPU & GPU Algorithms via DPC++ Interfaces:

  • K-Means Clustering
  • K-Nearest Neighbor (KNN)
  • Support Vector Machines (SVM) with Linear and Radial Basis Function (RBF) Kernels
  • Principal Components Analysis (PCA)
  • Density-based Special Clustering of Applications with Noise (DBSCAN)
  • Random Forest

Benchmarks

This library addresses all stages of the data analytics pipeline: preprocessing, transformation, analysis, modeling, validation, and decision-making.

 

Specifications

Processors:

  • Intel® Core™ processors
  • Intel® Xeon® processors

GPUs:

  • Intel® Processor Graphics Gen9 and above
  • Xe architecture

Operating systems:

  • Linux*
  • Windows*
  • macOS*

Compilers:

  • Intel® oneAPI DPC++/C++ Compiler
  • Intel® C++ Compiler
  • GNU Compiler Collection (GCC)* on Linux
  • Microsoft Visual C++ Compiler* on Windows
  • Clang on macOS

Languages:

  • Data Parallel C++ (DPC++) 
    Note Must have Intel® oneAPI Base Toolkit installed
  • C++
  • Java
  • Python*

For more information, see the system requirements.

Get the Single Component

A stand-alone version of this component is available.

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Runtime Version

Get Help

Your success is our success. Access these support resources when you need assistance.

 

Open-Source Version

Intel oneAPI Data Analytics Library is available as an open-source library.

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Product and Performance Information

1

Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.

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