using our util::_Fmt front-end helps to reduce the code size,
since all usages rely on a single inclusion of boost::format
including boost::format via header can cause quite some code bloat
NOTE: partial solution, still some further includes to reorganise
this draft fills in the structure how to get from an invocation
of the engine service to the starting of actual CalcStream instances.
Basically the EngineService implementation is repsonsile to
instruct the Segmentation to provide a suitable Dispatcher.
...this was quite insidious, but most of the problems
were in the test fixture. Treating the root context
on re-creation is something to be carefull though
While this isn't immediately relevant to the problem at hand,
it looks like a sensible idea to be able to explore
an existing data structure by iterators exposing pointers
(instead of reference wrappers).
Generally speaking, reference wrappers would be preferrable,
but, especially when the data structure relies on STL containers,
the default constructed values for resizing rule out
the standard reference wrapper, which can't be default
constructed. Using a custom variant would be equivalent
to using just a plain pointer (since both can be NULL and can be rebound)
...for the very specific situation when we want
to explore an existing data structure, and the
exploration assumes value semantics.
The workaround then is to use pointers as values.
This test setup is intended to emulate the situation
when adding jobs to the scheduler; thus we should use
an implicit sequence as root element.
I.e. we have to treat a wood, not a single tree
Note: test still fails, since we take a copy
of a Node object somewhere inadvertently
...attempt to build it based on the monadic iterator primitives.
Only problem is: need to find out relation between nodes
after the fact. In the real usage situation, this
is not a problem, since we have a state object
there, which can track the relation as it is established
there was the possibility for the random offset added in this test
to add up to a whole frame, which would cause the
re-quantisation to wrap to the next fame (and thus the
CHECK in line 110 to fail.
DispatcherInterface_test now passes the compiler,
meaning that the interfaces are completely defined,
all the generated types are OK and all operations are
at least stubbed.
Replacing all those stubs will be the next step
decision: the base for any deadline calculations
is the expected real time corresponding to the grid origin.
This value is contained in the Timings record.
this clarifies the relation of TimeAnchor and Timings,
the latter act as a general spec and abstracted grid,
while the latter actually performs the conversion and
deadline checking
the buildsystem will now pick up and link
all test cases according to the layer, e.g.
backend tests will automatically be linked
against the backend + library solely.
tests used to be defined ad hoc and test definitions
are scattered confusingly over various directories.
Now built some simple rules into the buildsystem
to allow organising the tests into layers and
linking them accordingly.
Note: this switches to building shared objects
for the test classes too, which effectively speeds up
both re-building and re-running of test cases
while refactoring, I thought it might be a good idea
only to use Query objects. But in this special case,
most often you'd just want to pass in a simple query
with a literal query string. So this convenience shortcut
indeed makes sense.
This template was a leftover from the early days
of Lumiera development and doesn't provide any
substantial value as an abstraction.
For the more intricate cases, we're using the
lib::MultiFact template, which allows to install
several "fabrication" functions at runtime
the solution is to introduce a superinterface
and let Dispatcher augment that with the specific parts.
This way, the Job planning only has to rely on the
rather generic stuff (TimeAnchor, FrameCoord)
NOTE: this commit makes the whole JobPlanning machinery
compilable for the first time!
brainstorming how to implement the job planning stage
the idea is to built on top of the IterExplorer,
but have the "stack" of re-evaluation integrated
into a custom type, which exploits the static
node network structure to avoid heap allocations
solution idea: again use a builder function?
this enables expansion of a (functional) data structure
until exhaustion -- which is what we need to
build job functors by traversing and expanding
an arbitrarily nested job definition structure
the intention is to use this to simplify
generating render jobs based on the elaborated
dependency network of the render nodes. The key
challenge is to overcome the necessity to
store partially done evaluations as
continuation
the tricky part seems to be how to combine the
source iterators into a new monad instance, while
keeping this "Combinator" Strategy configurable
...just passes the compiler, while still lacking
even the generic implementation of joining
together the source iterators
The idea is to avoid building a data structure
for intermediary results, while still being able
to process a variably sized and arbitrary shaped
set of source data
implemented as extension to the linear combinations.
I decided to use the same "always floor" rule
as employed for time quantisation. Moreover,
we don't support floating point, only rationals
funny enough this possible memory corruption
didn't happen in the unit test, because my
compiler optimised the additional int field
of class SubDummy, making it the same size
of the baseclass. Now matters should be safe.
also touches the question how to represent the job
descriptor datastructure. @Cehteh: I've just pasted
in your preliminary data struct definitinons
from the relevant mailing list discussions.
