Use Intel TBB's parallel_sort even for nested parallelism.

TBB has a global task scheduler (that's one of the reason TBB is not
linked statically but dyanmically instead). This allows control over all
running threads, enabling us to use nested parallelism and the scheduler
doing all the task allocation itself.

That is, nested parallel execution such as in

    parallel_for(seq, [](const auto& rng){
      parallel_sort(rng);
    });

is no problem at all, as the scheduler still claims control over the
global environment.

Therefore, use `parallel_sort` Range overload where possible.

References:

- https://www.threadingbuildingblocks.org/docs/help/hh_goto.htm#reference/algorithms.htm
- https://www.threadingbuildingblocks.org/docs/help/hh_goto.htm#reference/algorithms/parallel_sort_func.htm
- https://www.threadingbuildingblocks.org/docs/help/hh_goto.htm#reference/task_scheduler.htm
- https://www.threadingbuildingblocks.org/docs/help/hh_goto.htm#reference/task_scheduler/task_scheduler_init_cls.htm
- https://www.threadingbuildingblocks.org/docs/help/hh_goto.htm#tbb_userguide/Initializing_and_Terminating_the_Library.htm

tools/components.cpp

@@ -143,7 +143,7 @@ int main(int argc, char *argv[])
         std::vector<TarjanEdge> graph_edge_list;
         auto number_of_nodes = LoadGraph(argv[1], coordinate_list, graph_edge_list);
 
-        tbb::parallel_sort(graph_edge_list.begin(), graph_edge_list.end());
+        tbb::parallel_sort(graph_edge_list);
         const auto graph = std::make_shared<TarjanGraph>(number_of_nodes, graph_edge_list);
         graph_edge_list.clear();
         graph_edge_list.shrink_to_fit();