Suitor matcher #734
Suitor matcher #734
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Does the increase in runtime performance really justify the templated implementation? Can't you just branch on a sortSuitor variable at runtime? Also, the sortEdgesByWeight method is not appropriate for |
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I removed the templated implementation, it that was not really necessary.
I moved it in |
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michitux
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In general this looks good now, I have just a few small comments that you may implement or not.
Do you have any idea how fast sorting neighbors by id with your new function is compared to the existing sortEdges functionality? My suspicion would be that due to increased locality it may be faster in practice, in particular when multiple cores are used. If it should be faster, I would suggest to replace the existing sortEdges method by your new method.
| * @return @a edge weight to the i-th (outgoing) neighbor of @a u, or @c +inf if no such | ||
| * neighbor exists. | ||
| */ | ||
| edgeweight getIthNeighborWeight(node u, index i) const { |
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Is it intentional that this method is not added to the Python interface?
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getIthNeighbor does not have a Python interface, so we either provide a Python interface for both or for none.
include/networkit/graph/Graph.hpp
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| const auto sortAdjacencyArrays = [&](node u, std::vector<node> &adjList, | ||
| std::vector<edgeweight> &weights, | ||
| std::vector<edgeid> &edgeIds) -> void { | ||
| std::vector<index> indices(adjList.size()); |
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Would it be faster to have one indices vector per thread that is only re-allocated when a node with a larger degree is encountered? The design with the new indices vector for every call is definitely clearer, what I'm wondering though is what impact this memory allocation has on performance.
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Although the code slightly changed in the most recent version, I think time can still be safed if resize is only done if adjList.size() > indices.size(). Reasoning: resize calls erase on indices if adjList.size() < indices.size(), while iota is called anyways afterwards.
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Now resize is called only when necessary.
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This PR brings the 1/2-approximation algorithm for maximum (weighted) matching by Manne and Halappanavar (New Effective Multithreaded Matching Algorithms, IPDPS 2014).
There are two versions of the algorithm, one of which only supports graphs where the adjacency lists of every vertex are sorted by edge weight in decreasing order. To this end I also added a new
sortEdgesByWeightmethod inGraph, which allows to sort the edges of a graph by edge weight in increasing or decreasing order. This method uses a new genericsortEdgesmethod inGraph, which allows to sort the edges of a graph according to a custom criterion.I am a bit hesitant to add the new sorting methods to
Graph, they are probably too specific. An alternative idea is to implement them inGraphTools.