This is a trick to get much better scheduling and timing guesses.
Instead of targeting a specific level, rather a fixed number of nodes
is processed in each chunk, yet still always processing complete levels.
The final level number to expect can be retrieved from the chain-load graph.
With this refactoring, we can now schedule a wake-up job precisely
after the expected completion of the last level
Invent a special JobFunctor...
- can be created / bound from a λ
- self-manages its storage on the heap
- can be invoked once, then discards itself
Intention is to pass such one-time actions to the Scheduler
to cause some ad-hoc transitions tied to curren circumstances;
a notable example will be the callback after load-test completion.
In the first draft version, a blocked Gate was handled by
»polling« the Gate regularly by scheduling a re-invocation
repeatedly into the future (by a stepping defined through
ExecutionCtx::getWaitDelay()).
Yet the further development of the Activity-Language indicates
that the ''Notification mechanism'' is sufficient to handle all
foreseeable aspects of dependency management. Consequently this
''Gate poling is no longer necessary,'' since on Notification
the Gate is automatically checked and the activation impulse
is immediately passed on; thus the re-scheduled check would
never get an opportunity actually to trigger the Gate; such
an active polling would only be necessary if the count down
latch in the Gate is changed by "external forces".
Moreover, the first Scheduler integration tests with TestChainLoad
indicate that the rescheduled polling can create a considerable
additional load when longer dependency chains miss one early
prerequisite, and this additional load (albeit processed
comparatively fast by the Scheduler) will be shifted along
needlessly for quite some time, until all of the activities
from the failed chain have passed their deadline. And what
is even more concerning, these useless checks have a tendency
to miss-focus the capacity management, as it seems there is
much work to do in a near horizon, which in fact may not be
the case altogether.
Thus the Gate implementation is now *changed to just SKIP*
when blocked. This helped to drastically improve the behaviour
of the Scheduler immediately after start-up -- further observation
indicated another adjustment: the first Tick-duty-cycle is now
shortened, because (after the additional "noise" from gate-rescheduling
was removed), the newly scaled-up work capacity has the tendency
to focus in the time horizon directly behind the first jobs added
to the timeline, which typically is now the first »Tick«.
ð¡ this leads to a recommendation, to arrange the first job-planning
chunk in such a way that the first actual work jobs appear in the area
between 5ms and 10ms after triggering the Scheduler start-up.Scheduler¡
Introducing a fixed pre-delay on each new Calc-Streem seemed like an obvious remedy,
yet on closer investigation it turned out that the start-up logic as such was contradictory,
which was only uncovered by some rather special schedule patterns.
After fixing the logic deficiencies, Scheduler starts up as intended
and the probabilistic capacity-control seems to work as designed.
Thus no need to introduce an artificial delay at begin, even while
this implies that typically the first round of job-planning will be
performed synchronous, in the invoking thread (which may be surprising,
but is completely within the limits of the architecture; we do not
employ specifically configured threads and planning should be done
in short chunks, thus the first chunk can well be done by the caller)
The first complete integration test with Chain-Load
highlighted some difficulties with the overall load regulation:
- it works well in the standard case (but is possibly to eager to scale up)
- the scale-up sometimes needs several cycles to get "off the ground"
- when the first job is dispatched immediately instead of going
through the queue, the scheduler fails to boot up
- prime diagnostics with the first time invocation
- print timings relative to this first invocation
- DUMP output to watch the crucial scheduling operations
... so this (finally) is the missing cornerstone
... traverse the calculation graph and generate render jobs
... provide a chunk-wise pre-planning of the next batch
... use a future to block the (test) thread until completed
- decided to abstract the scheduler invocations as λ
- so this functor contains the bare loop logic
Investigation regarding hash-framework:
It turns out that boost::hash uses a different hash_combine,
than what we have extracted/duplicated in lib/hash-value.hpp
(either this was a mistake, or boost::hash did use this weaker
function at that time and supplied a dedicated 64bit implementation later)
Anyway, should use boost::hash for the time being
maybe also fix the duplicated impl in lib/hash-value.hpp
- use a dedicated context "dropped off" the TestChainLoad instance
- encode the node-idx into the InvocationInstanceID
- build an invocation- and a planning-job-functor
- let planning progress over an lib::UninitialisedStorage array
- plant the ActivityTerm instances into that array as Scheduling progresses
Introduced as remedy for a long standing sloppiness:
Using a `char[]` together with `reinterpret_cast` in storage management helpers
bears danger of placing objects with wrong alignment; moreover, there are increasing
risks that modern code optimisers miss the ''backdoor access'' and might apply too
aggressive rewritings.
With C++17, there is a standard conformant way to express such a usage scheme.
* `lib::UninitialisedStorage` can now be used in a situation (e.g. as in `ExtentFamily`)
where a complete block of storage is allocated once and then subsequently used
to plant objects one by one
* moreover, I went over the code base and adapted the most relevant usages of
''placement-new into buffer'' to also include the `std::launder()` marker
... special rule to generate a fixed expansion on each seed
... consecutive reductions join everything back into one chain
... can counterbalance expansions and reductions
...as it turns out, the solution embraced first was the cleanest way
to handle dynamic configuration of parameters; just it did not work
at that time, due to the reference binding problem in the Lambdas.
Meanwhile, the latter has been resolved by relying on the LazyInit
mechanism. Thus it is now possible to abandon the manipulation by
side effect and rather require the dynamic rule to return a
''pristine instance''.
With these adjustments, it is now possible to install a rule
which expands only for some kinds of nodes; this is used here
to crate a starting point for a **reduction rule** to kick in.
It seams indicated to verify the generated connectivity
and the hash calculation and recalculation explicitly
at least for one example topology; choosing a topology
comprised of several sub-graphs, to also verify the
propagation of seed values to further start-nodes.
In order to avoid addressing nodes directly by index number,
those sub-graphs can be processed by ''grouping of nodes'';
all parts are congruent because topology is determined by
the node hashes and thus a regular pattern can be exploited.
To allow for easy processing of groups, I have developed a
simplistic grouping device within the IterExplorer framework.
- with the new pruning option, start-Nodes can now be anywhere
- introduce predicates to detect start-Nodes and exit-Nodes
- ensure each new seed node gets the global seed on graph construction
- provide functionality to re-propagate a seed and clear hashes
- provide functionality to recalculate the hashes over the graph
up to now, random values were completely determined by the
Node's hash, leading to completely symmetrical topology.
This is fine, but sometimes additional randomness is desirable,
while still keeping everything deterministic; the obvious solution
is to make the results optionally dependent on the invocation order,
which is simply to achieve with an additional state field. After some
tinkering, I decided to use the most simplistic solution, which is
just a multiplication with the state.
...so this was yet another digression, caused by the desire
somehow to salvage this problematic component design. Using a
DSL token fluently, while internally maintaining a complex and
totally open function based configuration is a bit of a stretch.
For context: I've engaged into writing a `LazyInit` helper component,
to resolve the inner contradiction between DSL use of `RandomDraw`
(implying value semantics) and the design of a processing pipeline,
which quite naturally leads to binding by reference into the enclosing
implementation.
In most cases, this change (to lazy on-demand initialisation) should be
transparent for the complete implementation code in `RandomDraw` -- with
one notable exception: when configuring an elaborate pipeline, especially
with dynamic changes of the probability profile during the simulation run,
then then obviously there is the desire to use the existing processing
pipeline from the reconfiguration function (in fact it would be quite
hard to explain why and where this should be avoided). `LazyInit` breaks
this usage scenario, since -- at the time the reconfiguration runs --
now the object is not initialised at all, but holds a »Trojan« functor,
which will trigger initialisation eventually.
After some headaches and grievances (why am I engaging into such an
elaborate solution for such an accidental and marginal topic...),
unfortunately it occurred to me that even this problem can be fixed,
with yet some further "minimal" adjustments to the scheme: the LazyInit
mechanism ''just needs to ensure'' that the init-functor ''sees the
same environment as in eager init'' -- that is, it must clear out the
»Trojan« first, and it ''could apply any previous pending init function''
fist. That is, with just a minimal change, we possibly build a chain
of init functors now, and apply them in given order, so each one
sees the state the previous one created -- as if this was just
direct eager object manipulation...
...this is a more realistic demo example, which mimics
some of the patterns present in RandomDraw. The test also
uses lambdas linking to the actual storage location, so that
the invocation would crash on a copy; LazyInit was invented
to safeguard against this, while still allowing leeway
during the initialisation phase in a DSL.
...oh my.
This is getting messy. I am way into danger territory now....
I've made a nifty cool design with automatically adapted functors;
yet at the end of the day, this does not bode well with a DSL usage,
where objects appear to be simple values from a users point of view.
- Helper function to find out of two objects are located
"close to each other" -- which can be used as heuristics
to distinguish heap vs. stack storage
- further investigation shows that libstdc++ applies the
small-object optimisation for functor up to »two slots«
in size -- but only if the copy-ctor is trivial. Thus
a lambda capturing a shared_ptr by value will *always*
be maintained in heap storage (and LazyInit must be
redesigned accordingly)...
- the verify_inlineStorage() unit test will now trigger
if some implementation does not apply small-object optimisation
under these minimal assumptions
the RandomDraw rules developed last days are meant to be used
with user-provided λ-adapters; employing these in a context
of a DSL runs danger of producing dangling references.
Attempting to resolve this fundamental problem through
late-initialisation, and then locking the component into
a fixed memory location prior to actual usage. Driven by
the goal of a self-contained component, some advanced
trickery is required -- which again indicates better
to write a library component with adequate test coverage.
...now using the reworked partial-application helper...
...bind to *this and then recursively re-invoke the adaptation process
...need also to copy-capture the previously existing mapping-function
first test seems to work now
Investigation in test setup reveals that the intended solution
for dynamic configuration of the RandomDraw can not possibly work.
The reason is: the processing function binds back into the object instance.
This implies that RandomDraw must be *non-copyable*.
So we have to go full circle.
We need a way to pass the current instance to the configuration function.
And the most obvious and clear way would be to pass it as function argument.
Which however requires to *partially apply* this function.
So -- again -- we have to resort to one of the functor utilities
written several years ago; and while doing so, we must modernise
these tools further, to support perfect forwarding and binding
of reference arguments.
- strive at complete branch coverage for the mapping function
- decide that the neutral value can deliberately lie outside
the value range, in which case the probability setting
controls the number of _value_ result incidents vs
neutral value result incidents.
- introduce a third path to define this case clearly
- implement the range setting Builder-API functions
- absorb boundrary and illegal cases
For sake of simplicity, since this whole exercise is a byproduct,
the mapping calculations are done in doubles. To get even distribution
of values and a good randomisation, it is thus necessary to break
down the size_t hash value in a first step (size_t can be 64bit
and random numbers would be subject to rounding errors otherwise)
The choice of this quantiser is tricky; it must be a power of two
to guarantee even distribution, and if chosen to close to the grid
of the result values, with lower probabilities we'd fail to cover
some of the possible result values. If chosen to large, then
of course we'd run danger of producing correlated numbers on
consecutive picks.
Attempting to use 4 bits of headroom above the log-2 of the
required value range. For example, 10-step values would use
a quantiser of 128, which looks like a good compromise.
The following tests will show how good this choice holds up.
This highly optimised function was introduced about one year ago
for handling of denomals with rational values (fractions), as
an interim solution until we'll switch to C++20.
Since this function uses an unrolled loop and basically
just does a logarithmic search for the highest set bit,
it can just be declared constexpr. Moreover, it is now
relocated into one of the basic utility headers
Remark: the primary "competitor" is the ilogb(double),
which can exploit hardware acceleration. For 64bit integers,
the ilog2() is only marginally faster according to my own
repeated invocation benchmarks.
The first step was to allow setting a minimum value,
which in theory could also be negative (at no point is the
code actually limited to unsigned values; this is rather
the default in practice).
But reconsidering this extensions, then you'd also want
the "neutral value" to be handled properly. Within context,
this means that the *probability* controls when values other
than the neutral value are produced; especially with p = 1.0
the neutral value shall not be produced at all
...since the Policy class now defines the function signature,
we can no longer assume that "input" is size_t. Rather, all
invocations must rely on the generic adaptaion scheme.
Getting this correct turns out rather tricky again;
best to rely on a generic function-composition.
Indeed I programmed such a helper several years ago,
with the caveat that at that time we used C++03 and
could not perfect-forward arguments. Today this problem
can be solved much more succinct using generic Lambdas.
to define this as a generic library component,
any reference to the actual data source moust be extracted
from the body of the implementation and supplied later
at usage site. In the actual case at hand the source
for randomness would be the node hash, and that is
absolutely an internal implementation detail.
The idea is to use some source of randomness to pick a
limited parameter value with controllable probability.
While the core of the implementation is nothing more
than some simple numeric adjustments, these turn out
to be rather intricate and obscure; the desire to
package these technicalities into a component
however necessitates to make invocations
at usage site self explanatory.
...these were developed driven by the immediate need
to visualise ''random generated computation patterns''
for ''Scheduler load testing.''
The abstraction level of this DSL is low
and structures closely match some clauses of the DOT language;
this approach may not yet be adequate to generate more complex
graph structures and was extracted as a starting point
for further refinements....
With all the preceding DSL work, this turns out to be surprisingly easy;
the only minor twist is the grouping of nodes into (time)levels,
which can be achieved with a "lagging" update from the loop body
Note: next step will be to extract the DSL helpers into a Library header
...refine the handling of FrameRates close to the definition bounds
...implement the actual rule to scale allocator capacity on announcement
...hook up into the seedCalcStream() with a default of +25FPS
+ test coverage
...whenever a new CalcStream is seeded, it would be prudent
not only to step up the WorkForce (which is already implemented),
but also to provide a hint to the BlockFlow allocator regarding
the expected calculation density.
Such a hint would allow to set a more ample »epoch« spacing,
thereby avoiding to drive the allocator into overload first.
The allocator will cope anyway and re-balance in a matter of
about 2 seconds, but avoiding this kind of control oscillations
altogether will lead to better performance at calculation start.
The test case "scheduleRenderJob()" -- while deliberately operated
quite artificially with a disabled WorkForce (so the test can check
the contents in the queue and then progress manually -- led to discovery
of an open gap in the logic: in the (rare) case that a new task is
added ''from the outside'' without acquiring the Grooming-Token, then
the new task could sit in the entrace queue, in worst case for 50ms,
until the next Scheduler-»Tick« routinely sweeps this queue. Under
normal conditions however, each dispatch of another activity will
also sweep the entrance queue, yet if there happens to be no other
task right now, a new task could be stuck.
Thinking through this problem also helped to amend some aspects
of Grooming-Token handling and clarified the role of the API-functions.
Use a simple destructor-trick to set up a concise notation
for temporarily manipulating a value for testing.
The manipulation will automatically be undone
when leaving scope
For now, the `EngineObserver` is defined as an empty shell,
outfitted with a low-level binary message dispatch API.
Messages are keyed by a Symbol, which allows evolution of private message types.
Routing and Addressing is governed by an opaque size_t hash.
The `EngineEvent` data base class provides »4 Slots« of inline binary storage;
concrete subclasses shall define the mapping of actual data into this space
and provide a convenience constructor for events.
For use by the Scheduler, a `WorkTiming`-Event is defined based on this scheme;
this allows to implement the λ-work and λ-done of the Scheduler-`ExecutionCtx`.
These hooks will be invoked at begin and end of any render calculations.
