Graph Visualization: Added the General Chunk algorithm, multiple stacks to address debug markers, colors for nodes and unit testing. #2550
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| func addDebugMarker(builder *labelForVulkanCommands, from, to int) { | ||
| level := builder.labelsInsideDebugMarkers[len(builder.labelsInsideDebugMarkers)-1].GetSize() - 1 | ||
| builder.numberOfDebugMarker++ | ||
| func (dm *debugMarker) addDebugMarker(from, to int) { |
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I still don't really understand how addDebugMarker and checkDebugMarkers are working. A few comments might help.
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I implemented a different way to handle the debug markers, I think now it is very simple to understand the idea, also I added comments to better understanding.
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| type chunk struct { |
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I think this struct and the fields could use brief descriptions of their purpose. The types and names by themselves do not by themselves make it very clear what these variables represent or how they are used. The other structs holding somewhat complex internal data structures like this could also probably use some brief comments.
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I added a brief descriptions of structs and fields , most of them in regard to the chunk algorithm.
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| func (g *graph) joinNodesInChunksByFrame() { |
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Is there a reason that joining nodes by frame, and making the chunks have to be done together like this? Could we have separate joinNodesByFrame() and makeChunks() steps?
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I separated them in two different functions, join joinNodesByFrame() and makeChunks(), in addition I added the config for the global variables used in the chunk algorithm.
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| func makeChunks(nodes []*node) { |
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I think this function could use a comment describing its purpose.
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I added a comment to describe the purpose, also I renamed some variables to better understanding of the code.
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| // for the range [0, 1, 2, 3, ...]. | ||
| positionToChunksID [][]int | ||
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| // builded checks if the K-ary tree was builded for this chunk. |
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You probably want built instead of builded
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You are right, I fixed this, thank you.
to address debug markers, colors for nodes and unit testing. General Chunk Algorithm: This algorithm creates super-nodes to avoid display more than a fix number of nodes in every level. For every level, the nodes are grouped in super-nodes based on command names, then if the numbers of nodes exceed the maximum limit, chunks are created, and if in the new chunks the numbers of nodes exceed the maximum limit, then new chunks are created and so on. Results: Applying the General Chunk Algorithm is possible to visualize graphs containing millions of nodes and edges. The time is reduced from ~20 seconds to ~2 seconds to expand super-nodes in Tensorboard. Note1: All "op" attribute for nodes in pbtxt format must be different, since the find_similar_subgraphs algorithm from Tensorboard computes the similarity between nodes with same "op". Note2: Adding colors to the nodes increase the time for expand super-nodes.
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