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Intel® oneAPI Data Analytics Library 2021.1
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@KalyanovD KalyanovD released this 14 Dec 12:01
2021.1
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The release contains all functionality of Intel® DAAL. See Intel® DAAL release notes for more details.

What's New

Library Engineering:

  • Renamed the library from Intel® Data Analytics Acceleration Library to Intel® oneAPI Data Analytics Library and changed the package names to reflect this.
  • Deprecated 32-bit version of the library.
  • Introduced Intel GPU support for both OpenCL and Level Zero backends.
  • Introduced Unified Shared Memory (USM) support

Introduced new Intel® oneDAL and daal4py functionality:

  • GPU:
    • Batch algorithms: K-means, Covariance, PCA, Logistic Regression, Linear Regression, Random Forest Classification and Regression, Gradient Boosting Classification and Regression, kNN, SVM, DBSCAN and Low-order moments
    • Online algorithms: Covariance, PCA, Linear Regression and Low-order moments
    • Added Data Management functionality to support DPC++ APIs: a new table type for representation of SYCL-based numeric tables (SyclNumericTable) and an optimized CSV data source

Improved Intel® oneDAL and daal4py performance for the following algorithms:

  • CPU:
    • Logistic Regression training and prediction
    • k-Nearest Neighbors prediction with Brute Force method
    • Logistic Loss and Cross Entropy objective functions

Added Technical Preview Features in Graph Analytics:

  • CPU:
    • Undirected graph without edge and vertex weights (undirected_adjacency_array_graph), where vertex indices can only be of type int32
    • Jaccard Similarity Coefficients for all pairs of vertices, a batch algorithm that processes the graph by blocks

Aligned the library with Intel® oneDAL Specification 1.0 for the following algorithms:

  • CPU/GPU:
    • K-means, PCA, kNN

Introduced new functionality for scikit-learn patching through daal4py:

  • CPU:
    • Acceleration of NearestNeighbors and KNeighborsRegressor scikit-learn estimators with Brute Force and K-D tree methods
    • Acceleration of TSNE scikit-learn estimator
  • GPU:
    • Intel GPU support in scikit-learn for DBSCAN, K-means, Linear and Logistic Regression

Improved performance of the following scikit-learn estimators via scikit-learn patching:

  • CPU:
    • LogisticRegression fit, predict and predict_proba methods
    • KNeighborsClassifier predict, predict_proba and kneighbors methods with “brute” method

Known Issues

  • Intel® oneDAL DPC++ APIs does not work on GEN12 graphics with OpenCL backend. Use Level Zero backend for such cases.
  • train_test_split in daal4py patches for Scikit-learn can produce incorrect shuffling on Windows*
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