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The last step is pretty simple. All that's left to do is to take the
ordered edge container and to `append` their points to their `Path`
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Now that the problematic edges are dealt with, all the normal edges
need to also get routed, but first, it's a good idea to order them.
As such, the DAG is consumed to produce the list of all the edges in
the optimal order for them to get routed.
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Now that every single node has gotten its exact position, the only
thing left to do is to route all the edges between them. First of
all, the special edges (like corners) need to be handled.
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Now, that it's precisely known how much space is needed between each
pair of the layers, it's possible to set the vertical coordinates for
each of the nodes based on the layer heights (the height of the
longest node within said layer) and the number of horizontal lanes
required after it.
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Now that the nodes are placed into the grid (at least horizontally),
it's possible to focus on the edges. Since, the information about
the number of edges going from/to each of the layers is known, it's
possible to determine the number of horizontal space needed to placed
those.
In this part of the layouter, the focus is on three different laning
points:
- horizontal lanes between layers.
- entry lanes for each of the nodes.
- exit lanes for each of the nodes.
Indexation is done independently for each of the lane types.
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Since the predetermined order of the nodes for each layer was already
predetermined, what's left is to put their coordinates based on the
size of the nodes and the predefined margins between them.
Except the resulting layouts are extremely left-heavy. As such, it's
important to use the coordinate selection part to push as many nodes
to the right as possible to balance the graph out at least somewhat.
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Since from this point on, no breaking changes will be done to the graph
It's possible to finalize the layout. Meaning that from this point
onwards the logical positions of the nodes remain unchanged.
Having this guarantee we can proceed to calculating their real
positions, starting from the horizontal coordinates.
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Now that the layer permutation is finalized, it's possible to select
a couple of node-groups that only depend on a single predecessor and
consider them a single linear segment. This allows to "draw" straight
vertical lines accross the layout for nodes that could benefit from
that.
Such linear segments are also paramount for good looking long edges
(both forwards and backwards facing).
\note: this part of the layouter could still use more work.
If you're looking to rework it, this is the spot.
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To optimize the algorithms coming from now, having a topological
of the nodes of the graph is benefitial. And, considering, no new
nodes will be added to the graph from this point on, we can only
compute it once.
To optimize the ordering even further, augmented graph is used.
On top of the original (and earlier added artifical) edges, the graph
used for obtaining the ordering get a few extra edges added.
`llvm::ReversePostOrderTraversal` is used to convert the graph to the
ordered node list.
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To ensure the optimal layout is choosen, it's important to try out
and compare a few different permutations.
First of all, layers are optimized. Those layers, that only contain
irrelevant artificial nodes are removed and all the others are shifted
in order to maintain simple layer indexing.
Then, using a simple hill climbing the permutation with the lowest
number of edge crossings is selected. After which a technique based
on computing barycenters for nodes and layers is used as a fallback
to ensure even the least optimal layout is still usable.
On top of all that, simple clustering based on the classification
obtained while preparing the graph is used to keep original nodes
somewhat separate from the artificial ones.
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Preparation includes:
- ensuring there are no loops.
- ensuring there are no edges spanning more than a single layer.
- ensuring there are no backwards facing edges that were not split
into a bunch of parts to simplify laying them out.
- ensuring there are no self loops (they are treated similarly to
backwards facing edges, which they theoretically are).
Subproducts include:
- `Classifier` allowing to cheaply determine whether a given node is
adjacent to an artificial edge.
- `Ranks` container allowing to easily determine the layer each of the
nodes belongs to.
Remove code in revng.cli.model_dump and override_by_name that was used
when the model had tags. The code subclassed the default pyYAML loader
to ignore unknown tags to allow reading the model data easily. Since the
model no longer has tags this code is dead and can be removed.
Since PipelineC is not thread-safe, add a lock to all function calls to
it to avoid any thread safety-related issues.
At the same time, use a thread pool to run "expensive" PipelineC
functions in GraphQL (specifically `produce_target` and `run_analysis`)
to avoid problems due to the cooperative nature of Python coroutines.
This commit adds the newly implemented functionality in PipelineC both
in revng.api and the graphql api, allowing:
* retrieval of global variable names
* unwrapping of a single target
* execution of analyses
Ivan Krysak