Before You Begin

oneDAL is located in <install_dir>/dal directory where <install_dir> is the directory in which Intel® oneAPI Base Toolkit was installed.

The current version of oneDAL with SYCL support is available for Linux* and Windows* 64-bit operating systems. The prebuilt oneDAL libraries can be found in the <install_dir>/dal/<version>/redist directory.

To learn about the system requirements and the dependencies needed to build examples, refer to the System Requirements page.

End-to-end Example

Below you can find a typical usage workflow for a oneDAL algorithm on GPU. The example is provided for Principal Component Analysis algorithm (PCA).

The following steps depict how to:

• Read the data from CSV file

• Run the training and inference operations for PCA

• Access intermediate results obtained at the training stage

1. Include the following header that makes all oneDAL declarations available.

   #include "oneapi/dal.hpp"
   
   /* Standard library headers required by this example */
   #include <cassert>
   #include <iostream>

2. Create a SYCL* queue with the desired device selector. In this case, GPU selector is used:

   const auto queue = sycl::queue{ sycl::gpu_selector{} };

3. Since all oneDAL declarations are in the oneapi::dal namespace, import all declarations from the oneapi namespace to use dal instead of oneapi::dal for brevity:

   using namespace oneapi;

4. Use CSV data source to read the data from the CSV file into a table:

   const auto data = dal::read<dal::table>(queue, dal::csv::data_source{"data.csv"});

5. Create a PCA descriptor, configure its parameters, and run the training algorithm on the data loaded from CSV.

   const auto pca_desc = dal::pca::descriptor<float>
      .set_component_count(3)
      .set_deterministic(true);
   
   const dal::pca::train_result train_res = dal::train(queue, pca_desc, data);

6. Print the learned eigenvectors:

   const dal::table eigenvectors = train_res.get_eigenvectors();
   
   const auto acc = dal::row_accessor<const float>{eigenvectors};
   for (std::int64_t i = 0; i < eigenvectors.row_count(); i++) {
   
      /* Get i-th row from the table, the eigenvector stores pointer to USM */
      const dal::array<float> eigenvector = acc.pull(queue, {i, i + 1});
      assert(eigenvector.get_count() == eigenvectors.get_column_count());
   
      std::cout << i << "-th eigenvector: ";
      for (std::int64_t j = 0; j < eigenvector.get_count(); j++) {
         std::cout << eigenvector[j] << " ";
      }
      std::cout << std::endl;
   }

7. Use the trained model for inference to reduce dimensionality of the data:

   const dal::pca::model model = train_res.get_model();
   
   const dal::table data_transformed =
      dal::infer(queue, pca_desc, data).get_transformed_data();
   
   assert(data_transformed.column_count() == 3);

Build and Run Examples

Perform the following steps to build and run examples demonstrating the basic usage scenarios of oneDAL with SYCL support. Go to <install_dir>/dal/<version> and then set up an environment as shown in the example below:

1. Set up the required environment for oneDAL (variables such as CPATH, LIBRARY_PATH, and LD_LIBRARY_PATH):

   • On Linux, there are two possible ways to set up the required environment: via vars.sh script or via modulefiles.

     • Setting up oneDAL environment via vars.sh script

       Run the following command:

       source ./env/vars.sh

     • Setting up oneDAL environment via modulefiles

       1. Initialize modules:

          source $MODULESHOME/init/bash

          NOTE:

          Refer to Environment Modules documentation for details.

       2. Provide modules with a path to the modulefiles directory:

          module use ./modulefiles

       3. Run the module:

          module load dal

   • On Windows, run the following command:

     /env/vars.bat

2. Copy ./examples/oneapi/dpc to a writable directory if necessary (since it creates temporary files):

   cp –r ./examples/oneapi/dpc ${WRITABLE_DIR}

3. Set up the compiler environment for Intel® oneAPI DPC++/C++ Compiler. See Get Started with Intel® oneAPI DPC++/C++ Compiler for details.

4. Build and run the examples that show how to use oneDAL with SYCL support:

   NOTE:

   You need to have write permissions to the examples folder to build examples, and execute permissions to run them. Otherwise, you need to copy examples/oneapi/dpc and examples/oneapi/data folders to the directory with right permissions. These two folders must be retained in the same directory level relative to each other.

   • On Linux:

     # Navigate to the directory containing examples and then build them:
     cd /examples/oneapi/dpc
     make so example=svm_two_class_thunder_dense_batch # This will compile and run Correlation example using Intel(R) oneAPI DPC++/C++ Compiler
     make so mode=build                         # This compiles all examples in the current directory

   • On Windows:

     # Navigate to the directory containing examples and then build them:
     cd /examples/oneapi/dpc
     nmake dll example=svm_two_class_thunder_dense_batch+ # This will compile and run Correlation example using Intel(R) oneAPI DPC++/C++ Compiler
     nmake dll mode=build                         # This compiles all examples in the current directory

   To see all available parameters of the build procedure, type make on Linux* or nmake on Windows*.

5. The resulting example binaries and log files are written into the _results directory.

   NOTE:

   You should run the examples from examples/oneapi/dpc folder, not from _results folder. Most examples require data to be stored in examples/oneapi/data folder and to have a relative link to it started from examples/oneapi/dpc folder.

   You can build traditional C++ examples located in examples/oneapi/cpp folder in a similar way.

Compile and build applications with pkg-config

The pkg-config tool is a widely used tool for building software with dependencies. Intel® oneAPI Data Analytics Library provides files with pkg-config metadata for compiling and linking an application to the library.

Set up the environment

To use pkg-config, build the library and then set up the environment using vars.sh or vars.bat scripts:

• On Linux: source ./env/vars.sh

• On Windows: /env/vars.bat

Choose a metadata file

The metadata files provided by oneDAL cover only host device configuration on 64-bit Linux, macOS, or Windows operating system for C++.

Choose the metadata file based on oneDAL threading mode and linking method you use:

Compile a program using pkg-config

To compile a test.cpp program with oneDAL and pkg-config, provide the name of the oneDAL pkg-config metadata file as an input parameter. For example:

• On Linux or macOS:

  icc test.cpp pkg-config --cflags --libs dal-dynamic-threading-host

• On Windows:

  for /F "delims=," %i in ('pkg-config --cflags --libs dal-dynamic-threading-host) do icl test.cpp %i

A sample code for svm_two_class_thunder_dense_batch example with SYCL support. Run the following from the examples/oneapi/cpp directory:

• On Linux or macOS:

  icc -I source/ source/svm/svm_two_class_thunder_dense_batch.cpp icc test.cpp pkg-config --cflags --libs dal-dynamic-threading-host

• On Windows:

  for /F "delims=," %i in ('pkg-config --cflags --libs dal-dynamic-threading-host) do icl -I source/ icl svm_two_class_thunder_dense_batch.cpp %i

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