It seemed like we're doomed...
Yet we barely escaped our horrid fate, because the C++ structured bindings happen to look also for get<i> member functions!
Any other solution involving a free function `get<i>(h)` would not work, since the `std::tuple` used as base class would inevitably drag in std::get via ADL
Obviously, the other remedy would be to turn the `StorageFrame` into a member; yet doing so is not desirable, as makes the actual storage layout more obscure (and also more brittle)
Actually it is the implementation of `std::get` from our STL implementation
which causes the problems; our new custom implementation works as intended an
would also be picked by the compiler's overload resolution. But unfortunately,
the bounds checking assertion built into std::tuple_element<I,T> triggers
immediately when instantiated with out-of-bounds argument, which happens
during the preparation of overload resolution, even while the compiler
would pick another implementation in the following routine.
So we're out of luck and need to find a workaround...
Why is our specialisation of `std::get` not picked up by the compiler?
* it must somehow be related to the fact that `std::tuple` itself is a base class of lib::HeteroData
* if we remove this inheritance relation, our specialisation is used by the compiler and works as intended
* however, this strange behaviour can not be reproduced in a simple synthetic setup
It must be some further subtlety which marks the tuple case as preferrable
Seems like low hanging fruit and would especially allow to use
those storage blocks with ''structural bindings''
Providing the necessary specialisations for `std::get` however turns out to be difficult;
the compiler insists on picking the direct tuple specialisation, since std::tuple is a
protected base class; yet still surprising -- I was under the impression
that the direct overload should be the closest match
This basically solves this implementation challenge:
It was possible to construct a ''compile-time type-safe'' overlay,
while using force-casts ''without any metadata'' at runtime.
Obviously this is a dangerous setup, but ''should be resonably safe'' when used within the defined scheme...
* now yields an instance of the full `HeteroData<X,X,Z>` template
* work around problems with std::tuple_element_t for derived classes
Can now default-create and direct-init a front-End data block,
access and modify its elements — and the API looks ok-ish for me
Decision to use the generic case as short-hand for the first data block,
and thus ''hide the more technical Loki-List specialisations''
With that, I'm finally able to write the first test case...
This was a tough nut to crack, but recalling the actual usage situation was helpful
* the ''constructor type'' must be created / picked-up beforehand
* we are about to build a ''parameter-computation node''
* so this constructor presumably is passed to a type parameter of a specific weaving pattern
* the constructor must be invoked directly to drop-off the new data frame into the local scope
* it is preferable to attach it only in a second step to the existing HeteroData-Chain (residing in `TurnoutSystem`)
What would be ''desirable'' though is to have some additional safeguard in the type system
to prevent attaching the newly constructed block to a chain with a non-fitting layout,
i.e. the case when several constructors or types get mixed up (because without any further
safe-guard this would lead to uncoordinated out-of-bounds memory access)
- the Accessor is pretty much obvious: it carries the type from the enclosing scope and delegates to the generic accessor there
- the Constructor however is much more challenging, because it must construct the chained type ahead, and prepare a constructor functor that can be applied ''later'' to the actual data chain
The idea is to build an intrusive linked list of »storage frames«, each of which holds a tuple of arbitrarily typed values.
For such a compound, the C++ »tuple protocol« can be implemented, recursively, serving as base for all actual data access...
...as part of the rendering process, executed on top of the
low-level-model (Render Node network) as conceived thus far.
Parameter handling could be ''encoded'' into render nodes altogether,
or, at the other extreme, an explicit parameter handling could be specified
as part of the Render Node execution. As both extremes will lead to some
unfavourable consequences, I am aiming at a middle ground: largely, the
''automation computation'' will be encoded and hidden within the network,
implying that this topic remains to be addressed as part of defining
the Builder semantics and implementation. Yet in part the required
processing structure can be foreseen at an abstract level, and thus
the essential primitive operations are specified explicitly as part
of the Render Node definition. Notably the ''standard Weaving Pattern''
will include a ''parameter tuple'' into each `FeedManifold` and require
a binding function, which accepts this tuple as first argument.
Moreover — at implementation level, a library facility must be provided
to support handling of ''arbitrary heterogeneous data values'' embedded
directly into stack frame memory, together with a type-safe compile-time
overlay, which allows the builder to embed specific ''accessor handles''
into functor bindings, even while the actual storage location is not
yet known at that time (obviously, as being located on the stack).
__Note__: a recurring topic is how to return descriptor objects from builder functions; for this purpose, I am adjusting the semantics of `lib/nocopy.hpp` to be more specific...
During Render Node invocation, automation parameter data must be maintained.
For the simple standard path, this just implies to store the ''absolute nominal Time''
directly in the invoking stack frame and let some parameter adaptors do the translation.
However, it is conceivable to have much more elaborate translation functions,
and thus we must be prepared to handle an arbitrary number of parameter slots,
where each slot has arbitrary storage requirements.
The conclusion is to start with an intrusive linked list of overflow buckets.
This is an attempt to take aim at the next step,
which is to fill in the missing part for an actual node invocation...
''...still fighting to get ahead, due to complexity of involced concerns...''
This was an extended digression into architecture planning,
which became necessary in order to suitably map out the role
for the `TurnoutSystem` — which can now be defined as ''mediator''
to connect and forward control- and parameter data while specific
render invocation proceeds through the render node network.
Bugfix: should use the full bit-range for randomised data in `TestFrame`
Bugfix: prevent division by zero for approximate floatingpoint equality
...and use the new zip()-itertor to simplify the loops
As follow-up from the preceding refactorings,
it is now possible to drastically simplify several type signatures.
Generally speaking, iterator pipelines can now pass-through the result type,
and thus it is no longer necessary to handle this result type explicitly
In the case of `IterStateWrapper`, the result type parameter was retained,
but moved to the second position and defaulted; sometimes it can be relevant
to force a specific type; this is especially useful when defining an
`iterator` and a `const_iterator` based on the same »state-core«
For sake of completeness, since the `IterExplorer` supports building extended
search- and evaluation patterns, a tuple-zipping adapter can be expected
to handle these extended usages transparently.
While the idea is simple, making this actually happen had several ramifications
and required to introduce additional flexibility within the adaptor-framework
to cope better with those cases were some iterator must return a value, not a ref.
In the end, this could be solved with a bit of metaprogramming based on `std::common_type`
...and indeed, this is all quite nasty stuff — in hindsight, my initial intuition
to shy away from this topic was spot-on....
This involves some quite tricky changes in the way types are composed to form an iterator-pipeline.
Some wrappers are added as adaptors or for additional safety-checks, and to provide a builder-API.
Unfortunately, when building a new `IterExplorer` iterator pipeline from an existing pipeline naively,
composing all those types will add several unecessary intermediary wrapper-layers.
Worse even, the handling of `BaseAdapter` prevents the new tuple-zipping iterator
actually to pass-through any `expandChildren()` call.
These issues are a consequence of using templated types, instead of fixed types with an interface;
we can not just determine if some wrapper is present — unless the wrapper itself ''helps by exposing a tag.''
Even while I must admit that the whole packaging and adaptation machinery of `IterExplorer`
looks dangerously complex already, using dedicated type tags for this single purpose
seems like a tenable soulution.
There is an insidious problem when the Transformer takes references to internal state
within upstream iterators or state core. This problem only manifests when
a invariant based filtering or grouping operation is added after the Transformer,
because such an operation (notably Filter) will typically attempt to establish
the invariant from the constructor (to avoid dangling state). Unfortunately
doing so involves pulling data ''before the overall pipeline is moved into final location''
A workaround is to make the Transformer ''disengage'' on copy, so to provoke
a refresh and new pull in the new location after the copy / move / swap.
This only works if the transformer function as such is idempotent.
and yes ... this revealed a **long standing bug**
The `Filter::pullFilter()` invocation in the ctor may produce dangling refs,
whenever an underlying source-iterator generates a reference that points
into the iterator itself.
The reason is: due to the »onion shell« design of the iterator pipeline,
we are bound to move a source iterator into the next layer constructor.
With this minor change, the internal result-tuple may now also hold references,
in case a source iterator exposes a reference (which is in fact the standard case).
Under the right circumstances, source-manipulation through the iterator becomes possible.
Moreover, the optimiser should now be able to elide the result-value tuple in many cases.
and access the iterator internals directly instead.
Obviously this is an advanced and possibly dangerous feature, and only possible
when no additional transformer functions are interspersed; moreover this prompted
a review of some long standing type definitions to more precisely reflect the intention.
Note: most deliberately, the Transformer element in IterExplorer must expose a reference type,
and capture the results into an internal ItemWrapper. This is the only way we can support arbitrary functions.
Indeed the solution worked out yesterday could be extracted and turned generic.
Some in-depth testing is necessary though, and possibly some qualifications to allow pass-through of references...
Moreover, last days I started collecting notes regarding problem solving patterns,
which I tend to use frequently, but which might not be obvious and thus can easily
be forgotten. In fact, I had encountered several cases, where I did invent some
roughly similar solution repeatedly, having forgotten about already settled matters.
Hopefully the habit of collecting notes and hints at a central location serves to remedy
Basically I am sick of writing for-loops in those cases
where the actual iteration is based on one or several data sources,
and I just need some damn index counter. Nothing against for-loops
in general — they have their valid uses — sometimes a for-loop is KISS
But in these typical cases, an iterator-based solution would be a
one-liner, when also exploiting the structured bindings of C++17
''I must admit that I want this for a loooooong time —''
...but always got intimidated again when thinking through the fine points.
Basically it „should be dead simple“ — as they say
Well — — it ''is'' simple, after getting the nasty aspects of tuple binding
and reference data types out of the way. Yesterday, while writing those
`TestFrame` test cases (which are again an example where you want to iterate
over two word sequences simultaneously and just compare them), I noticed that
last year I learned about the `std::apply`-to-fold-expression trick, and
that this solution pattern could be adapted to construct a tuple directly,
thereby circumventing most of the problems related to ''perfect forwarding''
So now we have a new util function `mapEach` (defined in `tuple-helper.hpp`)
and I have learned how to make this application completely generic.
As a second step, I implemented a proof-of-concept in `IterZip_test`,
which indeed was not really challenging, because the `IterExplorer`
is so very sophisticated by now and handles most cases with transparent
type-driven adaptors. A lot of work went into `IterExplorer` over the years,
and this pays off now.
The solution works as follows:
* apply the `lib::explore()` constructor function to the varargs
* package the resulting `IterExplorer` instantiations into a tuple
* build a »state core« implementation which just lifts out the three
iterator primitives onto this ''product type'' (i.e. the tuple)
* wrap it in yet another `IterExplorer`
* add a transformer function on top to extract a value-tuple for each ''yield'
As expected, works out-of-the-box, with all conceivable variants and wild
mixes of iterators, const, pointers, references, you name it....
PS: I changed the rendering of unsigned types in diagnostic output
to use the short notation, e.g. `uint` instead of `unsigned int`.
This dramatically improves the legibility of verification strings.
I changed the rendering of unsigned types in diagnostic output
to use the short notation, e.g. `uint` instead of `unsigned int`.
This dramatically improves the legibility of verification strings.
Moreover, I took the opportunigy to look through the existing page
with codeing style guides to explicitly write down some conventions
formed over years of usage.
I did not just »make up« those light heartedly, rather these conventions
are the result of a craftsman's ''attentive observation and self-reflection.''
* based on reproducible data in `TestFrame`
* using Murmur64A hash-chaining to »mark« with a parameter
This emulates the simplest case of 1:1 processing and can also be applied ''in-place''
Change data layout to place a metadata record ''behind the'' payload data,
and add a checksum to allow for validating dummy calculations and also
detect data corruption on data modified after initial generation.
By virtue of a marker data word, the presence of a valid metadata record can be confirmed.
* Lumiera source code always was copyrighted by individual contributors
* there is no entity "Lumiera.org" which holds any copyrights
* Lumiera source code is provided under the GPL Version 2+
== Explanations ==
Lumiera as a whole is distributed under Copyleft, GNU General Public License Version 2 or above.
For this to become legally effective, the ''File COPYING in the root directory is sufficient.''
The licensing header in each file is not strictly necessary, yet considered good practice;
attaching a licence notice increases the likeliness that this information is retained
in case someone extracts individual code files. However, it is not by the presence of some
text, that legally binding licensing terms become effective; rather the fact matters that a
given piece of code was provably copyrighted and published under a license. Even reformatting
the code, renaming some variables or deleting parts of the code will not alter this legal
situation, but rather creates a derivative work, which is likewise covered by the GPL!
The most relevant information in the file header is the notice regarding the
time of the first individual copyright claim. By virtue of this initial copyright,
the first author is entitled to choose the terms of licensing. All further
modifications are permitted and covered by the License. The specific wording
or format of the copyright header is not legally relevant, as long as the
intention to publish under the GPL remains clear. The extended wording was
based on a recommendation by the FSF. It can be shortened, because the full terms
of the license are provided alongside the distribution, in the file COPYING.
⚠ __This is a problematic decision__
It temporarily **breaks compatibility with 32bit** until this issue is resolved.
== Explanation ==
Lumiera relies on a mix of the Standard library and Lib-Boost for calculation of hash values.
Before C++11, the Standard did not support and hashtable implementation; meanwhile, we
got several hash based containers in the STL and a framework for hashes,
which unfortunately is incomplete and cumbersome to use.
The C++ Committee has spend endless discussions and was not able to settle
on a convincing solution without major drawbacks regarding one aspect or the other.
This situation is problematic, since Lumiera relies heavily on the technique
of building stable systematic identifiers based on chained hash values.
It is thus essential to use a strong, reliable and portable hash function.
But unfortunately...
* the standard-fallback solution is known to be weak.
* Lib-Boost automatically uses stronger implementations for 64bit systems
* this implies that Hash-Values **are non-portable**
As the Lumiera project currently has no developer time to expend on such a
difficult and deep topic of fundamental research, today I decided to go down
the path of least resistance and **effectively abandon any system
that can not compile and use the 64bit `hash_combine` implementation.
This changeset extracts code from Lib-Boost 1.67 and adds a static assertion
to **break compilation** on non-64bit-platforms (whatever this means)
After augmenting our `lib/random.hpp` abstraction framework to add the necessary flexibility,
a common seeding scheme was ''built into the Test-Runner.''
* all tests relying on some kind of randomness should invoke `seedRand()`
* this draws a seed from the `entropyGen` — which is also documented in the log
* individual tests can now be launched with `--seed` to force a dedicated seed
* moreover, tests should build a coherent structure of linked generators,
especially when running concurrently. The existing tests were adapted accordingly
All usages of `rand()` in the code base were investigated and replaced
by suitable calls to our abstraction framework; the code base is thus
isolated from the actual implementation, simplifying further adaptation.
Last summer, I already identified a problmatic aspect
which could cause the Scheduler to fall idle without further notice:
5b62438eb4
Basically this situation should raise a **Scheduler-Emergency**,
but the only location where it can be easily detected is way down
in the implementation and has currently no clean way of signalling.
Moreover, how to handle a Scheduler-Emergency is likewise an open
question, an will in turn require even more cross-cutting notifications
and trigger actions somewhere at Render-Engine top-level.
By marking the location where this problem could be detected,
inadvertently I broke the SchedulerCommutator_test, which of course
must execute precisely this logic and check for the proper result.
Yet the problem as such is tricky and possibly far-reaching;
notably when processing long-running render jobs will reliably trigger
this situation — unless we establish additional dedicated control-logic
especially to cope with long-running jobs (opened #1382 for this topic)
__Bottom line__: we are far from addressing any of these issues right now,
and thus I reduced that failure to a warning message, so that at least
`SchedulerCommutator_test` passes again (it's not actually a defect there)
- SchedulerStress_test simply takes too long to complete (~4 min)
and is thus aborted by the testrunner. Add a switch to allow for
a quick smoke test.
- SchedulerCommutator_test aborts due to an unresolved design problem,
which I marked as failure
- add some convenience methods for passing arguments to tests
We use the memory address to detect reference to ''the same language object.''
While primarily a testing tool, this predicate is also used in the
core application at places, especially to prevent self-assignment
and to handle custom allocations.
It turns out that actually we need two flavours for convenient usage
- `isSameObject` uses strict comparison of address and accepts only references
- `isSameAdr` can also accept pointers and even void*, but will dereference pointers
This leads to some further improvements of helper utilities related to memory addresses...
Problems in `Rational_test` were caused by `#include' reorderings regarding ''rational'' and ''intgral'' numbers.
The actual root cause is the fact that `FSecs` is only a typedef,
which prevents us from providing a string conversion for rational numbers without ambiguity
* most usages are drop-in replacements
* occasionally the other convenience functions can be used
* verify call-paths from core code to identify usages
* ensure reseeding for all tests involving some kind of randomness...
__Note__: some tests were not yet converted,
since their usage of randomness is actually not thread-safe.
This problem existed previously, since also `rand()` is not thread safe,
albeit in most cases it is possible to ignore this problem, as
''garbled internal state'' is also somehow „random“
As it turns out, by far margin we mostly use rand() to generate
test values within a limited interval, using the ''modulo trick''
and thus excluding the upper bound.
Looking into the implementation of the distributions in the
libStdC++ shows that ''constructing'' a distribution on-the-fly
is cheap and boils down to checking and then storing the bounds;
so basically there is no need to keep ''cached distribution objects''
around, because for all practical purposes these behave like free functions
What is required occasionally is a non-zero HashValue, and sometimes
an interval of floating-point number or a normal distribution seem useful.
Providing these as free-standing convenience functions,
implicitly accessing the default PRNG.
* add new option to the commandline option parser
* pass this as std::optional to the test-suite constructor
* use this value optionally to inject a fixed value on re-seeding
* provide diagnostic output to show the actual seed value used
this seems to be the ''classical problem situation''
where a »clean« Dependency-Injection would require to waste storage
for a pointer to the same global resource in each and every distinct test class.
Since the Test-Suite is effectively global — even more so now due to
the reliance on "the" global `RandomSequencer` (PNRG) — we'll have to
bite the bullet and access a global static variable hidden behind teh scenes.
...to the base-class of all tests
* `seedRand()` shall be invoked by every test using randomisation
* it will draw a new seed for the implicit default-PRNG
* it will document this seed value
* but when a seed was given via cmdline, it will inject that instead
* `makeRandGen()` will create a new dedicated generator instance,
attached (by seeding) to the current default-PRNG
It is not clear yet how to pass the actual `SeedNucleus`, which
for obvious reasons must be maintained by the `test::Suite`
Using random or pseudo-random numbers as input for tests
can be a very effective tool to spot unintended behaviour in
corner cases, and also helps writing more principled test verifications.
However, investigating failures in randomised tests can be challenging.
A well-proven solution is to exploit the **determinism** of pseudo-random-numbers
by documenting a randomly generated seed, that can be re-injected for investigation.
Up to now, most tests rely on the old library function `rand()`, while
at some places already the C++ standard framework for random number generation
is used, packaged into a custom wrapper. Adding adequate support for
documented seed values seems to be easy to achieve, after switching
existing usages of `rand()` to a suitable drop-in replacement.
After some consideration, I decided ''against'' wiring random generator instances
explicitly, while allowing to do so on occasion, when necessary. Thus
the planned seeding mechanism will rather re-seed a ''implicit default''
generator, which could then be used to construct explicit generator instances
when required (e.g. for multithreaded tests)
As a starting point, this changeset replaces the `randomise()` API call
by a direct access to the ''reseeding functionality'' exposed by the
C++ framework and all default generators. Since we already provide a
dedicated static instance of the plattform entropy source, re-randomisation
can be achieved by seeding from there.
NOTE: there was extended debate in the net, questioning the viability
of the `std::random_seq` -- these arguments, while valid from a theoretical
point of view, seem rather moot when placed into a practical context,
where even 2^32 different generation-paths(cycles) are more than enough
to provide sufficient diffusion of results (unless the goal is really to
engage into Monte-Carlo simulations for scientific research or large model
simulations).
Notable most of the more catchy reprovals raised by Melissa O'Neill
have been refuted by experts of the field, even while being still propagated
at various places in the net, often combined with promoting PCG-Random.
Originally, this helper was called `IterIndex`, thereby following a
common naming scheme of iteration-related facilities in Lumiera, e.g.
* `IterAdapter`
* `IterExplorer`
* `IterSource`
However, I myself was not able to recall this name, and found myself
now for the second time unable to find this piece of code, even while
still able to recall vaguely that I had written something of this kind.
(and unable to find it by a text search for "index", for obvious reasons)
So, on a second thought, the original name is confusing: we do not create
an index of / for iterators; rather we are iterating an index. So this
is what it should be called...
showing the Node-symbol and a reduced rendering of
either the predecessor or a collection of source nodes.
For this we need functionality to traverse the node graph depth-first
and collect all leaf nodes (which are the source nodes without predecessor);
such can be implemented with the help of the expandAll() functionality
of `lib::IterExplorer`. In addition we need to collect, sort and deduplicate
all the source-node specs; since this is a common requirement, a new
convenience builder was added to `lib::IterExplorer`
...taking into account the prospecive usage context
where the builder expressions will be invoked from within
a media-library plug-in, using std::string_view to pass
the symbolic information seems like a good fit, because
the given spec will typically be assembled from some
building blocks, and thus in itself not be literal data.
...as follow-up to yesterday's decisions
- each Port will just feature a (stable) reference to a ProcID record
- which is deduplicated and likewise refers to deduplicated symbolic tags
- and further spec and hash values are computed on-demand by this entity
__Note__: all functionality belonging to the ''Builder'' can be assumed to run **non-concurrent**
Building a precise Frame Cache is a tough job, and is doomed to fail
when attempting to tie cache invalidation to state changes. The only
viable path is to create a system of systematic tagging of processing
steps, and use this as foundation for chained hash values, linked
in accordance to the actual processing structure.
This is complicated by the secondary concern of maintaining memory efficacy
for the render node model, which can be expected to grow to massive scale.
And even while this invocation can not be fully devised right now,
an attempt can be made to build a foundation that is not outright
wasteful, by detaching the logical information from the specific
weaving pattern used for implementation, and by minimising the
representation in memory and computing the compound information
on-demand....
Requirement analysis indicates that a »Node ID« is rather tangential
to the core operation of calculating media; the only infliction point
seems to be the generation of ''systematic cache keys.''
A spec — especially for the `Turnout` however is very relevant for
diagnostics, error reporting and unit testing. So we are in the
difficult situation where rather elaborate functionality is
required only for a secondary concern, and moreover the
node data structure imposes a critical memory leverage.
