Visible to Intel only — GUID: GUID-5A3D442D-71A1-48EA-B483-538C7F80E784
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_biased_batch.cpp
cov_dense_biased_online.cpp
cov_dense_online.cpp
csr_accessor.cpp
csr_table.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
heterogen_table.cpp
homogen_table.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_cov_dense_batch.cpp
pca_cov_dense_online.cpp
pca_precomputed_cor_dense_batch.cpp
pca_precomputed_cov_dense_batch.cpp
pca_svd_dense_batch.cpp
rbf_kernel_dense_batch.cpp
read_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_biased_batch.cpp
cov_dense_biased_online.cpp
cov_dense_online.cpp
csr_accessor.cpp
csr_table.cpp
dbscan_brute_force_batch.cpp
df_cls_dense_batch.cpp
df_reg_dense_batch.cpp
directed_graph.cpp
graph_service_functions.cpp
heterogen_table.cpp
homogen_table.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_cor_dense_batch.cpp
pca_cor_dense_online.cpp
pca_cov_dense_batch.cpp
pca_cov_dense_online.cpp
pca_precomputed_dense_batch.cpp
pca_svd_dense_batch.cpp
pca_svd_dense_online.cpp
polynomial_kernel_dense_batch.cpp
rbf_kernel_dense_batch.cpp
read_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
Density-Based Spatial Clustering of Applications with Noise
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
Visible to Intel only — GUID: GUID-5A3D442D-71A1-48EA-B483-538C7F80E784
Compressed Sparse Rows (CSR) Accessor
The csr_accessor class provides read-only access to the rows of the csr table as data arrays in CSR storage format.
Usage Example
#include <sycl/sycl.hpp>
#include <iostream>
#include "oneapi/dal/table/csr.hpp"
#include "oneapi/dal/table/csr_accessor.hpp"
namespace dal = oneapi::dal;
int main() {
sycl::queue queue { sycl::default_selector() };
// Create arrays of data, column indices and row offsets of the table
// in sparse CSR storage format with one-based indexing on host
const float host_data[] = { 1.0f, 2.0f, 3.0f, 4.0f, 1.0f, 11.0f, 8.0f };
const std::int64_t host_column_indices[] = { 1, 2, 4, 3, 2, 4, 2 };
const std::int64_t host_row_offsets[] = { 1, 4, 5, 7, 8 };
constexpr std::int64_t row_count = 4;
constexpr std::int64_t column_count = 4;
constexpr std::int64_t element_count = 7;
// Allocate SYCL shared memory for storing data, column indices and row offsets arrays
auto shared_data = sycl::malloc_shared<float>(element_count, queue);
auto shared_column_indices = sycl::malloc_shared<std::int64_t>(element_count, queue);
auto shared_row_offsets = sycl::malloc_shared<std::int64_t>(row_count + 1, queue);
// Copy data, column indices and row offsets arrays from host to SYCL shared memory
auto data_event = queue.memcpy(shared_data, host_data, sizeof(float) * element_count);
auto column_indices_event = queue.memcpy(shared_column_indices,
host_column_indices,
sizeof(std::int64_t) * element_count);
auto row_offsets_event =
queue.memcpy(shared_row_offsets, host_row_offsets, sizeof(std::int64_t) * (row_count + 1));
auto table = dal::csr_table::wrap(queue,
shared_data,
shared_column_indices,
shared_row_offsets,
row_count,
column_count,
dal::sparse_indexing::one_based,
{ data_event, column_indices_event, row_offsets_event });
// Accessing second and third rows of the table
dal::csr_accessor<const float> acc{ table };
const auto [block_data, block_column_indices, block_row_offsets] = acc.pull(queue, { 1, 3 });
for (std::int64_t i = 0; i < block_data.get_count(); i++) {
std::cout << block_data[i] << ", ";
}
std::cout << 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;
for (std::int64_t i = 0; i < block_row_offsets.get_count(); i++) {
std::cout << block_row_offsets[i] << ", ";
}
std::cout << std::endl;
sycl::free(shared_data, queue);
sycl::free(shared_column_indices, queue);
sycl::free(shared_row_offsets, queue);
return 0;
}Programming Interface
All types and functions in this section are declared in the oneapi::dal namespace and available via the inclusion of the oneapi/dal/table/csr_accessor.hpp header file.
template<typenameT>classcsr_accessor
- Template Parameters
-
T – The type of data values in blocks returned by the accessor. Should be const-qualified for read-only access. An accessor supports at least float, double, and std::int32_t types of T.
Constructors
template<typenameU=T,std::enable_if_t<std::is_const_v<U>,int>=0>csr_accessor(constcsr_table&table)
Creates a read-only accessor object from the csr table. Available only for const-qualified T.
- Template Parameters
-
U – The type of data values in blocks returned by the accessor. Should be const-qualified for read-only access. An accessor supports at least float, double, and std::int32_t types of U.
- Parameters
-
table – Input CSR table.
Public Methods
std::tuple<array_d,array_i,array_i>pull(constrange&row_range={0,-1}, constsparse_indexingindexing=sparse_indexing::one_based)const
Provides access to the rows of the table in CSR format The method returns arrays that directly point to the memory within the table if it is possible. In that case, the arrays refer to the memory as immutable data. Otherwise, new memory blocks are allocated, and the data from the table rows is converted and copied into those blocks. In this case, arrays refer to the blocks as mutable data.
- Parameters
-
row_range – The range of rows that data is returned from the accessor.
indexing – The indexing scheme used to access the data in the returned arrays in CSR layout. Should be sparse_indexing::zero_based or sparse_indexing::one_based.
std::tuple<T*,I*,I*>pull(array_d&data, array_i&column_indices, array_i&row_offsets, constrange&row_range={0,-1}, constsparse_indexingindexing=sparse_indexing::one_based)const
Provides access to the rows of the table in CSR format The method returns arrays that directly point to the memory within the table if it is possible. In that case, the arrays refer to the memory as immutable data. Otherwise, new memory blocks are allocated, and the data from the table rows is converted and copied into those blocks. In this case, arrays refer to the blocks as mutable data. The method updates data, column_indices, and row_offsets arrays.
- Parameters
-
data – The block in which memory is reused (if it is possible) to obtain the values from the table.
column_indices – The block in which memory is reused (if it is possible) to obtain the column indices from the table.
row_offsets – The block in which memory is reused (if it is possible) to obtain the row offsets from the table. The memory of data, column_indices and row_offsets blocks are reset either when their size is not big enough, or when the blocks contain immutable data, or when direct memory from the table can be used. If the blocks are reset to use direct memory pointers from the object, they refer to those pointers as immutable memory blocks.
row_range – The range of rows that data is returned from the accessor.
indexing – The indexing scheme used to access the data in the returned arrays in CSR layout. Should be sparse_indexing::zero_based or sparse_indexing::one_based.