Visible to Intel only — GUID: GUID-A31C636C-ED7B-4C31-8A64-BE43B12AB6CE
basic_statistics_dense_batch.cpp
column_accessor_homogen.cpp
cor_dense_batch.cpp
cov_dense_batch.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
pca_cor_dense_batch.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
column_accessor_homogen.cpp
connected_components_batch.cpp
cor_dense_batch.cpp
cov_dense_batch.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
logloss_dense_batch.cpp
louvain_batch.cpp
pca_dense_batch.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
Mathematical Notations
Mathematical Notations
Correlation and Variance-Covariance Matrices
Mathematical Notations
Principal Components Analysis Transform
Singular Value Decomposition
Association Rules
Mathematical Notations
Mathematical Notations
Cholesky Decomposition
QR Decomposition
Outlier Detection
Distance Matrix
Distributions
Engines
Moments of Low Order
Mathematical Notations
Quality Metrics
Sorting
Normalization
Mathematical Notations
Visible to Intel only — GUID: GUID-A31C636C-ED7B-4C31-8A64-BE43B12AB6CE
jaccard_batch_app.cpp
/*******************************************************************************
* Copyright 2020 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 <iostream>
#include "tbb/global_control.h"
#include "tbb/parallel_for.h"
#include "example_util/utils.hpp"
#include "oneapi/dal/algo/jaccard.hpp"
#include "oneapi/dal/graph/service_functions.hpp"
#include "oneapi/dal/graph/undirected_adjacency_vector_graph.hpp"
#include "oneapi/dal/io/csv.hpp"
#include "oneapi/dal/table/homogen.hpp"
namespace dal = oneapi::dal;
/// Computes Jaccard similarity coefficients for the graph. The upper triangular
/// matrix is processed only as it is symmetic for undirected graph.
///
/// @param [in] g The input graph
/// @param [in] block_row_count The size of block by rows
/// @param [in] block_column_count The size of block by columns
template <class Graph>
void vertex_similarity_block_processing(const Graph &g,
std::int32_t block_row_count,
std::int32_t block_column_count);
int main(int argc, char **argv) {
// load the graph
const auto filename = get_data_path("graph.csv");
using graph_t = dal::preview::undirected_adjacency_vector_graph<>;
const auto graph = dal::read<graph_t>(dal::csv::data_source{ filename });
// set the block sizes for Jaccard similarity block processing
const std::int32_t block_row_count = 2;
const std::int32_t block_column_count = 5;
// set the number of threads
const std::int32_t tbb_threads_number = 4;
tbb::global_control c(tbb::global_control::max_allowed_parallelism, tbb_threads_number);
// compute Jaccard similarity coefficients for the graph
vertex_similarity_block_processing(graph, block_row_count, block_column_count);
return 0;
}
template <class Graph>
void vertex_similarity_block_processing(const Graph &g,
std::int32_t block_row_count,
std::int32_t block_column_count) {
// create caching builders for all threads
std::vector<dal::preview::jaccard::caching_builder> processing_blocks(
tbb::this_task_arena::max_concurrency());
// compute the number of vertices in graph
const std::int32_t vertex_count = dal::preview::get_vertex_count(g);
// compute the number of rows
std::int32_t row_count = vertex_count / block_row_count;
if (vertex_count % block_row_count) {
row_count++;
}
// parallel processing by rows
tbb::parallel_for(
tbb::blocked_range<std::int32_t>(0, row_count),
[&](const tbb::blocked_range<std::int32_t> &r) {
for (std::int32_t i = r.begin(); i != r.end(); ++i) {
// compute the range of rows
const std::int32_t row_range_begin = i * block_row_count;
const std::int32_t row_range_end = (i + 1) * block_row_count;
// start column ranges from diagonal
const std::int32_t column_begin = 1 + row_range_begin;
// compute the number of columns
std::int32_t column_count = (vertex_count - column_begin) / block_column_count;
if ((vertex_count - column_begin) % block_column_count) {
column_count++;
}
// parallel processing by columns
tbb::parallel_for(
tbb::blocked_range<std::int32_t>(0, column_count),
[&](const tbb::blocked_range<std::int32_t> &inner_r) {
for (std::int32_t j = inner_r.begin(); j != inner_r.end(); ++j) {
// compute the range of columns
const std::int32_t column_range_begin =
column_begin + j * block_column_count;
const std::int32_t column_range_end =
column_begin + (j + 1) * block_column_count;
// set block ranges for the vertex similarity algorithm
const auto jaccard_desc =
dal::preview::jaccard::descriptor<>().set_block(
{ row_range_begin, std::min(row_range_end, vertex_count) },
{ column_range_begin,
std::min(column_range_end, vertex_count) });
// compute Jaccard coefficients for the block
dal::preview::vertex_similarity(
jaccard_desc,
g,
processing_blocks[tbb::this_task_arena::current_thread_index()]);
// do application specific postprocessing of the result here
}
},
tbb::simple_partitioner{});
}
},
tbb::simple_partitioner{});
}