- some tests are notoriously flaky
- increase resource limits for the testsuite to allow stronger stress tests
- provide a "quick" variant of BufferProviderLoad_test with lowered requirements
Furthermore, investigate test scaffolding in `DummyBuilderContext`,
which was broken due to the clean-up in `DiagnosticOutputSlot`.
It seems that only some placeholder instances are required here,
so that we might be successful with just instantiating the `UnimplementedConnection`
__TODO__: need to supply slightly more implementation into the latter,
so that we're able at least to ''create'' such a connection stub.
...even while this situation is unlikely, it needs to be considered:
A worker thread might terminate unexpectedly, due to an error;
all thread-local objects will be destroyed automatically — while the
`EngineBufferManager` might be processing further allocations requested
by that very thread, and dispatch them towards an already destroyed `LocalMemPool`
This turns out to be a nasty problem, since the whole setup was created
to allow the workers to operate independently from each other; any handshake
or registration/deregistration mechanism would require a lock of sorts,
thereby defeating the whole purpose of the setup.
The best solution I could find is to rely on a instance marker hash,
that can be checked with an atomic read prior to each dispatch of a
memory allocation. The destructor of the local pool would then be able
to ''disengage'', by invalidating that hash.
To evaluate this solution, I developed this mechanism
as a free-standing library component, called `LiveMark`
This is a collection with unique-ownership,
and the ability to create all content objects in ''RAII style''
from the constructor call, using a ''population functor''.
It is often used to maintain a collection of resource handles,
and especially to start a series of threads (for testing).
With the variadic λ-captures from C++20, it is possible to rewrite
the most frequently used population functors to be easier to use.
Notably we can now bind constructor arguments, that will be passed
to each of the created elements — a prominent example being the
thread function when launching several threads. Furthermore,
I added a constructor variant that consumes a Lumiera Iterator.
Adding this kind of generic variants is possible now with C++20,
since overload resolution takes concepts into account.
Well, finally.
Lumiera Iterators were conceived as a concept from the very beginning,
and this idea dates back even before C++11. Furthermore, over time some
''trait templates'' have been developed, to detect if some template argument
might match the known pattern of functions associated with a Lumiera Iterator.
Meanwhile this concept is used pervasively in the whole code base, and also
the traits templates proved to be reliable. It therefore seems that the time is ripe
to gradually introduce a genuine **concept** `lum_iter`
I'll take a defensive approach here, since any mistake would lead to
a massive breakage of compilation. The new concept is defined simply
by delegating to to the tried-and-tested trait template, which is
also renamed `can_IterForEach` ⟼ `can_LumieraIter`
One immediate benefit is that the compiler is now able to select
overloads based on that concept, which allows to drop the tricky
selection of variants based on the SFINAE technique and `enable_if`.
For validation, one very heavily used case of that pattern was switched
in `lib/format-util.hpp', which notably lies in the code-path used by `util::join`
Since thread_local implies a static setup, we need a way how to
build a new service instance. Usually this would be a default construction,
yet there might be services with special init requirements.
Using an explicit template parameter for this setup configuration
allows to make the primary template parameter just the service itself,
which makes the default usage much clearer.
- Use a ''monostate'' as access point to thread-local service instances
- rely on a configuration-context to setup the actual service
- investigate runtime overhead using a multithreaded microbenchmark
This is a design study to find out what would be required
to build an opaque function wrapper, similar to `std::function`,
yet without using any heap memory ever.
Such a building block would allow for some interesting options
for a well structured design of elaborate data structures.
This changeset provides an implementation draft.
__Two important findings to note__:
* it is impossible to get away with just a single function pointer
* because at the very least we need to invoke the destructor of the
embedded delegate. Thus something akin to a "manager" is required.
* this implies that the component would not be so efficient as I hoped,
since it requires two pointers and requires to emit code for each
type of delegate.
* by making the component **NonCopyable**, I avoided the complexities
of virtual copy support. In case the latter is desired, it would be
much simpler to switch to a classical OO approach with a VTable
and use our `PolymorphicValue` wrapper to handle copy and cloning.
This commit is the aggregated result of extended research
that is only loosely related to current development topics.
One theme was the construction of nested data structures using tuples,
which faces some difficulties due to tuple manipulations being limited
almost entirely to compile-time processing. However, the barrier between
compile-time and runtime can be overcome with the help of a dispatcher
table -- which leads to the secondary problem to handle the diverse
tuple elements with a common, generic functor. Such a functor can be
constructed, if and only if those element types are ''reconcilable''
The STDLIB provides two metafunctions, `std::common_type` and
`std::common_refernce`, which however pose some challenges for the
task at hand, since they include additional processing. In the end,
a custom version of the ''basic type reconciliation'' was implemented
and tested, and then used as foundation to build a dispatcher table
for runtime-indexed access and iteration over tuples.
As an aside, several deeply hidden bugs in our library code were resolved,
and the tuple-helpers, tuple string rendering and associated utilities
were regrouped and modernised.
As result of this research work, the following components are provided:
- meta/common-result
- meta/fun-trampoline
- meta/tuple-index-adaptor
The detailed analysis of corner cases indicated a lot of hidden dangers
when stretching the `IterExplorer` design beyond its limits.
By deliberate choice, this design was kept open so that advanced cohesive
implementation structures can be built. It is even possible ''and tolerated''
to rely on ''references'' to some degree -- assuming however that the
referred memory location remains stable.
As it turned out, these basic assumptions can be broken by a combination
of read-ahead processing (as is necessary for grouping and filtering),
together with adaptor objects embedded as value, especially when
the resulting pipeline object is then moved or copied during evaluation.
While my general attitude towards such extremely clever implementation
constructs is "don't shoot yourself into your foot unless you need to" --
it seems prudent to clean-up some crappy code from the first attempts
towards an iterator pipeline -- notably some of the »Itertools«.
This expands the processing scheme developed thus far, so that
an arbitrary nested iterable can be handled, as long as it can be
adapted by the generic mechanisms provided by IterExplorer.
Notably there is now a type pattern match to distinguish various cases,
even spanning the ''rather esoteric'' possibility that an underlying
iterator produces STL collections by-value.
By examining these special cases, the limitations of the `IteratorExplorer`
design were highlighted, and a number of tricky bugs could be fixed.
This is a feature I wasn’t sure for a long time how best to tackle.
It’s worth noting that an initial attempt at `IterExplorer` was based
on the monad pattern, which turned out to be a mistake, since monads
are more of an anti-pattern, than being genuinely useful.
The actual use-case that made me reconsider such a feature it
to build an seamlessly chained sequence from several iterators.
It would be simple to develop a suitable utility as a stand-alone helper,
yet it seemed worth the effort to integrate this into the `IterExplorer`
ecosystem, due to the flexible automatic adaptation to iterable source
pipelines provided therein.
Going this route incurred some cost however, unveiling several defects
related to handling nested iterable types in the generic processing framework.
The old implementation relied on two layered APIs:
one for the client and one for the implementer of
the Allocation state. This is logically sound, yet
clumsy in terms of design, since the internal API
turns out to be void of actual meaning.
It seems possible to eliminate this confusing structure
by a clear separation of ''implementation realms''
* the ''mechanics'' of connection state management
* the actual implementation of the connection
The first can be provided as generic template subclass,
while the latter should be confined entirely into the
implementation of the `Connection` interface.
Doing so requires to keep each part of the implementation
strictly focused onto its purpose — something I obviously
was not able to see at the time when writing this old
implementation, notably because I was completely guided
and driven by the test/dummy implementation without
considering how an actual implementation of
low-level data output would work.
These are generic vehicles to verify computation structures.
Since they are in no way related to our models, they belong into »Vault« layer
Furthermore this reorganisation of test support components is prerequisite
to also move further generic facilities down into the Vault
This resolves a long-standing issue:
we could not use proper include paths for test-helper code that was defined alongside the tests.
Since some test-helper code must now be relocated into the »Vault«-Layer,
I took the opportunity to finally resolve this annoyance. The solution is two-fold
* introduce a dedicated test-support library
* remove the quirks that were necessary to support some plain-C tests
Together, this allows to raise the include-directory by one level,
and then to adjust all folder names so that they match the namespace
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
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
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
