Intel® oneAPI Data Analytics Library Developer Guide and Reference
ID
772611
Date
11/07/2023
Public
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basic_statistics_dense_batch.cpp
basic_statistics_dense_online.cpp
column_accessor_homogen.cpp
cor_dense_batch.cpp
cor_dense_online.cpp
cov_dense_batch.cpp
cov_dense_online.cpp
csr_accessor.cpp
dbscan_brute_force_batch.cpp
df_cls_hist_batch.cpp
df_cls_hist_batch_random.cpp
df_cls_traverse_model.cpp
df_reg_hist_batch.cpp
df_reg_hist_batch_random.cpp
df_reg_traverse_model.cpp
kmeans_init_dense.cpp
kmeans_lloyd_dense_batch.cpp
knn_cls_brute_force_dense_batch.cpp
knn_reg_brute_force_dense_batch.cpp
knn_search_brute_force_dense_batch.cpp
linear_kernel_dense_batch.cpp
linear_regression_dense_batch.cpp
linear_regression_dense_online.cpp
logistic_regression_dense_batch.cpp
pca_cor_dense_batch.cpp
pca_cor_dense_online.cpp
pca_precomputed_cor_dense_batch.cpp
pca_precomputed_cov_dense_batch.cpp
rbf_kernel_dense_batch.cpp
svm_two_class_thunder_dense_batch.cpp
basic_statistics_dense_batch.cpp
basic_statistics_dense_online.cpp
column_accessor_homogen.cpp
connected_components_batch.cpp
cor_dense_batch.cpp
cor_dense_online.cpp
cov_dense_batch.cpp
cov_dense_online.cpp
csr_accessor.cpp
dbscan_brute_force_batch.cpp
df_cls_dense_batch.cpp
df_reg_dense_batch.cpp
directed_graph.cpp
graph_service_functions.cpp
jaccard_batch.cpp
jaccard_batch_app.cpp
kmeans_init_dense.cpp
kmeans_lloyd_dense_batch.cpp
knn_cls_brute_force_dense_batch.cpp
knn_cls_kd_tree_dense_batch.cpp
knn_search_brute_force_dense_batch.cpp
linear_kernel_dense_batch.cpp
linear_regression_dense_batch.cpp
linear_regression_dense_online.cpp
logloss_dense_batch.cpp
louvain_batch.cpp
pca_dense_batch.cpp
pca_dense_online.cpp
pca_precomputed_dense_batch.cpp
polynomial_kernel_dense_batch.cpp
rbf_kernel_dense_batch.cpp
shortest_paths_batch.cpp
sigmoid_kernel_dense_batch.cpp
subgraph_isomorphism_batch.cpp
svm_multi_class_thunder_csr_batch.cpp
svm_multi_class_thunder_dense_batch.cpp
svm_nu_cls_thunder_csr_batch.cpp
svm_nu_cls_thunder_dense_batch.cpp
svm_nu_reg_thunder_csr_batch.cpp
svm_nu_reg_thunder_dense_batch.cpp
svm_reg_thunder_csr_batch.cpp
svm_reg_thunder_dense_batch.cpp
svm_two_class_smo_csr_batch.cpp
svm_two_class_smo_dense_batch.cpp
svm_two_class_thunder_csr_batch.cpp
svm_two_class_thunder_dense_batch.cpp
triangle_counting_batch.cpp
K-Means Clustering
Glossary
Correlation and Variance-Covariance Matrices
Principal Component Analysis
Principal Components Analysis Transform
Singular Value Decomposition
Association Rules
Kernel Functions
Expectation-Maximization
Cholesky Decomposition
QR Decomposition
Outlier Detection
Distance Matrix
Distributions
Engines
Moments of Low Order
Quantile
Quality Metrics
Sorting
Normalization
Optimization Solvers
Decision Forest
Decision Trees
Gradient Boosted Trees
Stump
Linear and Ridge Regressions
LASSO and Elastic Net Regressions
k-Nearest Neighbors (kNN) Classifier
Implicit Alternating Least Squares
Logistic Regression
Naïve Bayes Classifier
Support Vector Machine Classifier
Multi-class Classifier
Boosting
csr_accessor.cpp
/*******************************************************************************
* Copyright 2023 Intel Corporation
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*******************************************************************************/
#include <sycl/sycl.hpp>
#include <iostream>
#ifndef ONEDAL_DATA_PARALLEL
#define ONEDAL_DATA_PARALLEL
#endif
#include "oneapi/dal/table/csr_accessor.hpp"
#include "oneapi/dal/table/csr.hpp"
#include "example_util/dpc_helpers.hpp"
namespace dal = oneapi::dal;
void run(sycl::queue &q) {
constexpr std::int64_t row_count = 4;
constexpr std::int64_t column_count = 4;
constexpr std::int64_t element_count = 7;
// create arrays of data, column indices, and row offsets of the table
// in sparse CSR storage format on host
const float data_host[] = { 1.0f, 2.0f, 3.0f, 4.0f, 1.0f, 11.0f, 8.0f };
const std::int64_t column_indices_host[] = { 1, 2, 4, 3, 2, 4, 2 };
const std::int64_t row_offsets_host[] = { 1, 4, 5, 7, 8 };
// allocate SYCL shared memory for storing data, column indices, and row offset arrays
auto data = sycl::malloc_shared<float>(element_count, q);
auto column_indices = sycl::malloc_shared<std::int64_t>(element_count, q);
auto row_offsets = sycl::malloc_shared<std::int64_t>(row_count + 1, q);
// copy data, column indices, and row offset arrays from the host to the SYCL shared memory
auto data_event = q.memcpy(data, data_host, sizeof(float) * element_count);
auto column_indices_event =
q.memcpy(column_indices, column_indices_host, sizeof(std::int64_t) * element_count);
auto row_offsets_event =
q.memcpy(row_offsets, row_offsets_host, sizeof(std::int64_t) * (row_count + 1));
// create a sparse table in CSR format from arrays of data, column indices, and row offsets
// that are allocated in SYCL shared memory
auto table = dal::csr_table{ q,
data,
column_indices,
row_offsets,
row_count,
column_count,
dal::detail::make_default_delete<const float>(q),
dal::detail::make_default_delete<const std::int64_t>(q),
dal::detail::make_default_delete<const std::int64_t>(q),
dal::sparse_indexing::one_based,
{ data_event, column_indices_event, row_offsets_event } };
dal::csr_accessor<const float> acc{ table };
// pull the second and third rows of the sparse table
// the pulled rows have one-based indices by default
const auto [block_data, block_column_indices, block_row_offsets] = acc.pull(q, { 1, 3 });
std::cout << "Print the original sparse data table as 3 arrays in CSR storage format:"
<< std::endl;
std::cout << "Values of the table:" << std::endl;
for (std::int64_t i = 0; i < element_count; i++) {
std::cout << data_host[i] << ", ";
}
std::cout << std::endl << "Column indices of the table:" << std::endl;
for (std::int64_t i = 0; i < element_count; i++) {
std::cout << column_indices_host[i] << ", ";
}
std::cout << std::endl << "Row offsets of the table:" << std::endl;
for (std::int64_t i = 0; i < row_count + 1; i++) {
std::cout << row_offsets_host[i] << ", ";
}
std::cout << std::endl;
std::cout << std::endl << "Print 2 rows from CSR table as dense float arrays" << std::endl;
std::cout << "Values in the second and third rows of the table as dense float array:"
<< std::endl;
for (std::int64_t i = 0; i < block_data.get_count(); i++) {
std::cout << block_data[i] << ", ";
}
std::cout << std::endl
<< "Column indices of the data in the second and third rows from CSR table:"
<< std::endl;
for (std::int64_t i = 0; i < block_column_indices.get_count(); i++) {
std::cout << block_column_indices[i] << ", ";
}
std::cout << std::endl
<< "Row offsets of the second and third rows from CSR table:" << std::endl;
for (std::int64_t i = 0; i < block_row_offsets.get_count(); i++) {
std::cout << block_row_offsets[i] << ", ";
}
std::cout << std::endl;
}
int main(int argc, char const *argv[]) {
for (auto d : list_devices()) {
std::cout << "Running on " << d.get_platform().get_info<sycl::info::platform::name>()
<< ", " << d.get_info<sycl::info::device::name>() << "\n"
<< std::endl;
auto q = sycl::queue{ d };
run(q);
}
return 0;
}