tensorflow节点布放（device assignment of node）算法：simpler_placer

 1 // 1. First add all of the nodes. Note that steps (1) and (2)
 2 // requires two passes over the nodes because the graph (and hence
 3 // the constraints) may not be acyclic.  这里graph可能是有环的？
 4 for (Node* node : graph_->nodes()) {
 5     // Skip the source and sink nodes.
 6     if (!node->IsOp()) { continue; }
 7     status = colocation_graph.AddNode(*node);
 8     if (!status.ok()) return AttachDef(status, node->def());
 9   }
10 // 2. Enumerate the constraint edges, and use them to update the disjoint node set.         // disjoint set（并查集，即不相交的节点集合），一种树型数据结构，
11 ...

1 ColocationGraph maintains the connected components of a colocation constraint graph, and uses this information to assign a satisfying device placement to the nodes of the graph. 
2 The implementation uses the union- find algorithm to maintain the connected components efficiently and incrementally as edges (implied by ColocationGraph::ColocateNodes() invocations) are added.  
3 参考：并查集wiki

 1  3. For each node, assign a device based on the constraints in thedisjoint node set.
 2   std::vector<Device*> devices;
 3   std::vector<Node*> second_pass;
 4   for (Node* node : graph_->nodes()) {
 5     // Skip the source and sink nodes.
 6     if (!node->IsOp()) {
 7       continue;
 8     }
 9     // Skip nodes that already have an assigned name.
10     if (!node->assigned_device_name().empty()) {
11       continue;
12     }
13     // Heuristic A: prefer to place "generators" with their only
14     // consumers.
15     //
16     // If this is a node with no inputs and a single (non-ref)
17     // consumer, we save this for a second pass, so that the
18     // consumer‘s placement is chosen.
19     if (IsGeneratorNode(node)) {    // generator node: no input, one output, not a reference-type node
20       second_pass.push_back(node);
21       continue;
22     }
23     status = colocation_graph.GetDevicesForNode(node, &devices);
24     ...
25     // Returns the first device in sorted devices list so we will always
26     // choose the same device.
27     //
28     // TODO(vrv): Factor this assignment out into a pluggable
29     // algorithm, so that SimplePlacer is responsible for enforcing
30     // preconditions and we can experiment with other algorithms when
31     // given a choice of devices. Once we have a better idea of the
32     // types of heuristics we want to use and the information needed
33     // to perform good placement we can add an interface for this.
34     string assigned_device = devices[0]->name();
35     // Heuristic B: If the node only operates on metadata, not data,
36     // then it is desirable to place that metadata node with its
37     // input.
38     if (IsMetadataNode(node)) {   
39       // Make sure that the input device type is in the list of supported
40       // device types for this node.
41       const Node* input = (*node->in_edges().begin())->src();
42       // TODO(vrv): if the input is empty, consider postponing this
43       // node‘s assignment to the second pass, so that we handle the
44       // case where a metadata node‘s input comes from a backedge
45       // of a loop.
46       const string& input_device_name = input->assigned_device_name();
47       if (CanAssignToDevice(input_device_name, devices)) {
48         assigned_device = input_device_name;
49       }
50     }
51     AssignAndLog(assigned_device, node);   // 将assigned_device分配个node节点，在步骤3中没有对符合Heuristic A的GeneratorNode分配设备，而是在步骤4中完成的
52   }

1 bool IsGeneratorNode(const Node* node) {
2   return node->num_inputs() == 0 && node->num_outputs() == 1 && node->out_edges().size() == 1 && !IsRefType(node->output_type(0));
3 }

1 bool IsMetadataNode(const Node* node) {
2   const string& node_type = node->type_string();
3   return (node_type == "Size" || node_type == "Shape" || node_type == "Rank");
4 }

// 4. Perform a second pass assignment for those nodes explicitly skipped during the first pass.
...