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.
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.
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
Documentation & Code Samples
Code Samples
Learn how to access oneAPI code samples in a tool command line or IDE.
Reference Implementations
Specifications
Processors:
- Intel® Core™ processors
- Intel® Xeon® processors
GPUs:
- Intel® Processor Graphics Gen9 and above
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.
Get Help
Your success is our success. Access these support resources when you need assistance.
Ready to Get Started?
Open-Source Version
Intel oneAPI Data Analytics Library is available as an open-source library.
Product and Performance Information
Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.