When a function is executed for many elements (e.g. per point) it is often the case
that some parameters are different for every element and other parameters are
the same (there are some more less common cases). To simplify writing such
functions one can use a "virtual array". This is a data structure that has a value
for every index, but might not be stored as an actual array internally. Instead, it
might be just a single value or is computed on the fly. There are various tradeoffs
involved when using this data structure which are mentioned in `BLI_virtual_array.hh`.
It is called "virtual", because it uses inheritance and virtual methods.
Furthermore, there is a new virtual vector array data structure, which is an array
of vectors. Both these types have corresponding generic variants, which can be used
when the data type is not known at compile time. This is typically the case when
building a somewhat generic execution system. The function system used these virtual
data structures before, but now they are more versatile.
I've done this refactor in preparation for the attribute processor and other features of
geometry nodes. I moved the typed virtual arrays to blenlib, so that they can be used
independent of the function system.
One open question for me is whether all the generic data structures (and `CPPType`)
should be moved to blenlib as well. They are well isolated and don't really contain
any business logic. That can be done later if necessary.
This is the initial merge from the geometry-nodes branch.
Nodes:
* Attribute Math
* Boolean
* Edge Split
* Float Compare
* Object Info
* Point Distribute
* Point Instance
* Random Attribute
* Random Float
* Subdivision Surface
* Transform
* Triangulate
It includes the initial evaluation of geometry node groups in the Geometry Nodes modifier.
Notes on the Generic attribute access API
The API adds an indirection for attribute access. That has the following benefits:
* Most code does not have to care about how an attribute is stored internally.
This is mainly necessary, because we have to deal with "legacy" attributes
such as vertex weights and attributes that are embedded into other structs
such as vertex positions.
* When reading from an attribute, we generally don't care what domain the
attribute is stored on. So we want to abstract away the interpolation that
that adapts attributes from one domain to another domain (this is not
actually implemented yet).
Other possible improvements for later iterations include:
* Actually implement interpolation between domains.
* Don't use inheritance for the different attribute types. A single class for read
access and one for write access might be enough, because we know all the ways
in which attributes are stored internally. We don't want more different internal
structures in the future. On the contrary, ideally we can consolidate the different
storage formats in the future to reduce the need for this indirection.
* Remove the need for heap allocations when creating attribute accessors.
It includes commits from:
* Dalai Felinto
* Hans Goudey
* Jacques Lucke
* Léo Depoix
Instead of depending on static initialization order of globals use
static variables within functions. Those are initialized on first use.
This is every so slighly less efficient, but avoids a full class of problems.
Those optimizations work on the multi-function network level.
Not only will they make the network evaluation faster, but they also
simplify the network a lot. That makes it easier to understand the
exported dot graph.
A multi-function network is a graph data structure, where nodes are
multi-functions (or dummies) and links represent data flow.
New multi-functions can be derived from such a network. For that
one just has to specify two sets of sockets in the network that
represent the inputs and outputs of the new function.
It is possible to do optimizations like constant folding on this
data structure, but that is not implemented in this patch yet.
In a next step, user generated node trees are converted into a
MFNetwork, so that they can be evaluated efficiently for many particles.
This patch also includes some tests that cover the majority of the code.
However, this seems to be the kind of code that is best tested by some
.blend files. Building graph structures in code is possible, but is
not easy to understand afterwards.
Reviewers: brecht
Differential Revision: https://developer.blender.org/D8049
This adds the `MultiFunction` type and some smallish utility types that it uses.
A `MultiFunction` encapsulates a function that is optimized for throughput by
always processing many elements at once.
This is an important part of the new particle system, because it allows us to
execute user generated node trees for many particles efficiently.
Reviewers: brecht
Differential Revision: https://developer.blender.org/D8030
This adds a new `CPPType` that encapsulates information about how to handle
instances of a specific data type. This is necessary for the function evaluation
system, which will be used to evaluate most of the particle node trees.
Furthermore, this adds an `IndexMask` class which offers a surprisingly useful
abstraction over an array containing unsigned integers. It makes two assumptions
about the underlying integer array:
* The integers are in ascending order.
* There are no duplicates.
`IndexMask` will be used to "select" certain particles that will be
processed in a data-oriented way. Sometimes, operations don't have to
be applied to all particles, but only some, those that are in the indexed by
the `IndexMask`. The two limitations imposed by an `IndexMask` allow for
better performance.
Reviewers: brecht
Differential Revision: https://developer.blender.org/D7957
