Kl updates (#50)

* compute_comm_affinity

added test kl_bsp_cost

more tests for max_comm_datastructure

update

update node affinity

added test for affinity

update test

update

affinity tests

affinity tests

update

update

small performance optimization

more test, pre move data update

pre_move_comm_data

new unit test

update test

update tests

update

update

update lambda container, bsp_cost function

cost function correction

max_comm_datastructures fix

fix kl_bsp_cost test

update

update

update

update

more tests

update

update comm update

update

update

update

update

update

update

added more tests, cmpute working

update comm affinity implemenation

comm_affinity optimizations

enable tests

update test

debug output

update

update update

update

unit tests passing

cleaning

update datastructure

update

update numa

added mt test

* adding more new_nodes after move

comments

update

update

* simplification

Author: tonibohnlein

include/osp/auxiliary/io/hdag_graph_file_reader.hpp

@@ -29,6 +29,7 @@ limitations under the License.
 #include "osp/concepts/computational_dag_concept.hpp"
 #include "osp/graph_algorithms/directed_graph_util.hpp"
 #include "osp/auxiliary/io/filepath_checker.hpp"
+#include "osp/concepts/constructable_computational_dag_concept.hpp"
 
 namespace osp {
 namespace file_reader {

include/osp/bsp/scheduler/LocalSearch/KernighanLin_v2/comm_cost_modules/comm_cost_policies.hpp

@@ -0,0 +1,124 @@
+/*
+Copyright 2024 Huawei Technologies Co., Ltd.
+
+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.
+
+@author Toni Boehnlein, Benjamin Lozes, Pal Andras Papp, Raphael S. Steiner
+*/
+
+#pragma once
+
+#include <algorithm>
+#include <cassert>
+#include <vector>
+
+namespace osp {
+
+template<typename T>
+struct DefaultHasEntry {
+    static inline bool has_entry(const T &val) { return val != 0; }
+};
+
+template<typename T>
+struct DefaultHasEntry<std::vector<T>> {
+    static inline bool has_entry(const std::vector<T> &val) { return !val.empty(); }
+};
+
+/**
+ * @brief Generic container for tracking child processor assignments in a BSP schedule using vectors.
+ *
+ * This structure tracks information about children assigned to each processor.
+ * It uses a 2D vector for dense data.
+ */
+template<typename vertex_idx_t, typename ValueType = unsigned, typename HasEntry = DefaultHasEntry<ValueType>>
+struct generic_lambda_vector_container {
+
+    /**
+     * @brief Range adapter for iterating over non-zero/non-empty processor entries.
+     */
+    class lambda_vector_range {
+      private:
+        const std::vector<ValueType> &vec_;
+
+      public:
+        class lambda_vector_iterator {
+            using iterator_category = std::input_iterator_tag;
+            using value_type = std::pair<unsigned, ValueType>;
+            using difference_type = std::ptrdiff_t;
+            using pointer = value_type *;
+            using reference = value_type &;
+
+          private:
+            const std::vector<ValueType> &vec_;
+            unsigned index_;
+
+          public:
+            lambda_vector_iterator(const std::vector<ValueType> &vec) : vec_(vec), index_(0) {
+                while (index_ < vec_.size() && !HasEntry::has_entry(vec_[index_])) {
+                    ++index_;
+                }
+            }
+
+            lambda_vector_iterator(const std::vector<ValueType> &vec, unsigned index) : vec_(vec), index_(index) {}
+
+            lambda_vector_iterator &operator++() {
+                ++index_;
+                while (index_ < vec_.size() && !HasEntry::has_entry(vec_[index_])) {
+                    ++index_;
+                }
+                return *this;
+            }
+
+            value_type operator*() const { return std::make_pair(index_, vec_[index_]); }
+
+            bool operator==(const lambda_vector_iterator &other) const { return index_ == other.index_; }
+            bool operator!=(const lambda_vector_iterator &other) const { return !(*this == other); }
+        };
+
+        lambda_vector_range(const std::vector<ValueType> &vec) : vec_(vec) {}
+
+        lambda_vector_iterator begin() { return lambda_vector_iterator(vec_); }
+        lambda_vector_iterator end() { return lambda_vector_iterator(vec_, static_cast<unsigned>(vec_.size())); }
+    };
+
+    /// 2D vector: for each node, stores processor assignment info
+    std::vector<std::vector<ValueType>> node_lambda_vec;
+
+    /// Number of processors in the system
+    unsigned num_procs_ = 0;
+
+    inline void initialize(const vertex_idx_t num_vertices, const unsigned num_procs) {
+        node_lambda_vec.assign(num_vertices, std::vector<ValueType>(num_procs));
+        num_procs_ = num_procs;
+    }
+
+    inline void reset_node(const vertex_idx_t node) { node_lambda_vec[node].assign(num_procs_, ValueType()); }
+
+    inline void clear() { node_lambda_vec.clear(); }
+
+    inline bool has_proc_entry(const vertex_idx_t node, const unsigned proc) const {
+        return HasEntry::has_entry(node_lambda_vec[node][proc]);
+    }
+
+    inline ValueType &get_proc_entry(const vertex_idx_t node, const unsigned proc) {
+        return node_lambda_vec[node][proc];
+    }
+
+    inline ValueType get_proc_entry(const vertex_idx_t node, const unsigned proc) const {
+        return node_lambda_vec[node][proc];
+    }
+
+    inline auto iterate_proc_entries(const vertex_idx_t node) { return lambda_vector_range(node_lambda_vec[node]); }
+};
+
+} // namespace osp