Turns out that we need to implemented fine grained and explicit handling logic
to ensure that Activity planning only ever happens protected by the Grooming-Token.
This is in accordance to the original design, which dictates that all management tasks
must be done in »management mode«, which can only be entered by a single thread at a time.
The underlying assumption is that the effort for management work is dwarfed in comparison
to any media calculation work.
However, in
5c6354882d
...I discovered an insidious border condition, an in an attempt to fix it,
I broke that fundamental assumpton. The problem arises from the fact that we
do want to expose a *public API* of the Scheduler. Even while this is only used
to ''seed'' a calculation stream, because any further planning- and management work
will be performed by the workers themselves (this is a design decision, we do not
employ a "scheduler thread")
Anyway, since the Scheduler API ''is'' public, ''someone from the outside'' could
invoke those functions, and — unaware of any Scheduler internals — will
automatically acquire the Grooming-Token, yet never release it,
leading to deadlock.
So we need a dedicated solution, which is hereby implemented as a
scoped guard: in the standard case, the caller is a management-job and
thus already holds the token (and nothing must be done). But in the
rare case of an »outsider«, this guard now ''transparently'' acquires
the token (possibly with a blocking wait) and ''drops it when leaving scope''
...this feature seems to be no longer necessary now;
leaving the actual implementation in-code for the time being,
but removed it from the public access API.
The last round of refactorings yielded significant improvements
- parallelisation now works as expected
- processing progresses closer to the schedule
- run time was reduced
The processing load for this test is tuned in a way to overload the
scheduler massively at the end -- the result must be correct non the less.
There was one notable glitch with an assertion failure from the memory manager.
Hopefully I can reproduce this by pressing and overloading the Scheduler more...
The rework from yesterday turned out to be effective ... unfortunately
a bit to much: since now late follow-up notifications take precedence,
a single worker tends to process the complete chain depth-first, because
the first chain will be followed and processed, even before the worker
was able to post the tasks for the other branches. Thus this single
worker is the only one to get a chance to proceed.
After some consideration, I am now leaning towards a fundamental change,
instead of just fixing some unfavourable behaviour pattern: while the
language semantics remains the same, the scheduler should no longer
directly dispatch into the next chain **from λ-post**. That is, whenever
a POST / NOTIFY is issued from the Activity-chain, the scheduler goes
through prioritisation.
This has further ramifications: we do not need a self-inhibition mechanism
any more (since now NOTIFY picks up the schedule time of the target).
With these changes, processing seems to proceed more smoothly,
albeit still with lots of contention on the Grooming token,
at least in the example structure tested here.
While the recent refactoring...
206c67cc
...was a step into the right direction, it pushed too hard,
overlooking the requirement to protect the scheduler contents
and thus all of the Activity-chains against concurrent modification.
Moreover, the recent solution still seems not quite orthogonal.
Thus the handling of notifications was thoroughly reworked:
- the explicit "double-dispatch" was removed, since actual usage
of the language indicates that we only need notifications to
Gate (and Hook), but not to any other conceivable Activity.
- thus it seems unnecessary to turn "notification" into some kind
of secondary work mode. Rather, it is folded as special case
into the regular dispatch.
This leads to new processing rules:
- a POST goes into λ-post (obviously... that's its meaning)
- a NOTIFY now passes its *target* into λ-post
- λ-post invokes ''dispatch''
- and **dispatching a Gate now implies to notify the Gate**
This greatly simplifies the »state machine« in the Activity-Language,
but also incurs some limitations (which seems adequate, since it is
now clear that we do not ''schedule'' or ''dispatch'' arbitrary
Activities — rather we'll do this only with POST and NOTIFY,
and all further processing happens by passing activation
along the chain, without involving the Scheduler)
use a feature of the Activity-Language prepared for this purpose:
self-Inhibition of the Chain. This prevents a prerequisite-NOTIFY
to trigger a complete chain of available tasks, before these tasks
have actually reached their nominal scheduling time.
This has the effect to align the computations much more strictly
with the defined schedule
The main (test) thread is kept in a blocking wait until the
planned schedule is completed. If however the schedule overruns,
the wake-up job could just be triggered prematurely.
This can easily be prevented by adding a dependency from the last
computation job to the wake-up job. If the computation somehow
flounders, the SAFETY_TIMEOUT (5s) will eventually raise
an exception to let the test fail cleanly (shutting down
the Scheduler automatically)
...it seems impossible to solve this conundrum other than by
opening a path to override a contextual deadline setting from
within the core Activity-Language logic.
This will be used in two cases
- when processing a explicitly coded POST (using deadline from the POST)
- after successfully opening a Gate by NOTIFY (using deadline from Gate)
All other cases can now supply Time::NEVER, thereby indicating that
the processing layer shall use contextual information (intersection
of the time intervals)
...this is an interesting test failure, which highlights inconsistencies
with handling of deadlines when processing follow-up from NOTIFY-triggers
There was also some fuzziness related to the ''meaning'' of λ-post,
leading to at least one superfluous POST invocation for each propagation;
fixing this does not solve the problem yet removes unnecessary overhead
and lock-contention
...playing around with the graph for the Scheduler integration test
...single threaded run time seemed to behave irregular
...but in fact it is very close to what can be expected
based on an ''averaged node weight''
Fortunately its very simple to add that into the existing node statistics
Basically this is all done and settled already: this is the `usageExample()`
from `TestChainLoadTest`. However, the focus is slightly different here:
We want a demonstration that the Scheduler can work flawlessly through
a massive load. Thus the plan is to use much more challenging parameters,
and then lean back and watch what happens....
...which turns out to be due to the DUMP-Statements,
which seem to create quite some contention on their own.
Test cases with very tight schedule will slip away then;
without print statement everything is GREEN now
This partially reverts commit 72f11549e6.
"Chain-Load: Scheduler instrumentation for observation"
Hint: revert this changeset to re-introduce the print statements for diagnostic
* added benchmark over synchronous execution as point of reference
* verified running times and execution pattern
* Scheduler **behaves as expected** for this example
- Generally speaking, the calibration uses current baseline settings;
- There are now two different load generation methods, thus both must be calibrated
- Performance contains some socked and non-linear effects, thus calibration
should be done close to the work point, which can be achieved by incremental
calibration until the error is < 5%
Interestingly, longer time-base values run slightly faster than predicted,
which is consistent with the expectation (socket cost). And using a larger
memory block increases time values, which is also plausible, since
cache effects will be diminishing
..initial gauging is a tricky subject,
since existing computer's performance spans a wide scale
Allowing
- pre calibration -98% .. +190%
- single run ±20%
- benchmark ±5%
...which can be deliberately attached (or not attached) to the
individual node invocation functor, allowing to study the effect
of actual load vs. zero-load and worker contention
Within Chain-Load, the infrastructure to add this crucial feature
is minimal: each node gets a `weight` parameter, which is assigned
using another RandomDraw-Rule (by default `weight==0`).
The actual computation load will be developed as a separate component
and tied in from the node calculation job functor.
...during development of the Chain-Load, it became clear that we'll often
need a collection of small trees rather than one huge graph. Thus a rule
for pruning nodes and finishing graphs was added. This has the consequence
that there might now be several exit nodes scattered all over the graph;
we still want one single global hash value to verify computations,
thus those exit hashes must now be picked up from the nodes and
combined into a single value.
All existing hash values hard coded into tests must be updated
...with this change, processing is ''ahead of schedule'' from the beginning,
which has the nice side effect that the problematic contention situation
with these very short computation jobs can not arise, and most of the schedule
is processed by a single worker.
Processing pattern is now pretty much as expected
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
Scheduling a wake-up job behind the end of the planned schedule did the trick.
Sometimes there is ''strong contention'' immediately after full provision of the WorkForce,
but this seems to be as expected, since the »Jobs« currently used have no
actually relevant run time on their own. It is even more surprising that
the Capacity-control logic is able to cope with this situation in a matter
of just some milliseconds, bringing the average Lag at ~ 300µs
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¡
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
two rather obvious bugfixes
(well, after watching the Scheduler in action...)
- the first planning-chunk needs an offset
- the future to block on must be setup before any dispatch happens
- 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
- test setup without actual scheduler
- wire the callbacks such to verify
+ all nodes are touched
+ levels are processed to completion
+ the planning chunk stops at the expected level
+ all node dependencies are properly reported through the callbacks
- 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 ''special encoding'' to marshal the specific coordinates for this test setup
- use a fixed Frame-Grid to represent the ''time level''
- invoke hash calculation through a specialised JobFunctor subclass
The number of nodes was just defined as template argument
to get a cheap implementation through std::array...
But actually this number of nodes is ''not a characteristics of the type;''
we'd end up with a distinct JobFunctor type for each different test size,
which is plain nonsensical. Usage analysis reveals, now that the implementation
is ''basically complete,'' that all of the topology generation and statistic
calculation code does not integrate deeply with the node storage, but
rather just iterates over all nodes and uses the ''first'' and ''last'' node.
This can actually be achieved very easy with a heap-allocated plain array,
relying on the magic of lib::IterExplorer for all iteration and transformation.
- 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
Since Chain-Load shall be used for performance testing of the scheduler,
we need a catalogue of realistic load patterns. This extended effort
started with some parameter configurations and developed various graph
shapes with different degree of connectivity and concurrency, ranging
from a stable sequence of very short chains to large and excessively
interconnected dependency networks.
Through introduction of a ''pruning rule'', it is possible
to create exit nodes in the middle of the graph. With increased
intensity of pruning, it is possible to ''choke off'' the generation
and terminate the graph; in such a case a new seed node is injected
automatically. By combination with seed rules, an equilibrium of
graph start and graph termination can be achieved.
Following this path, it should be possible to produce a pattern,
which is random but overall stable and well suited to simulate
a realistic processing load.
However, finding proper parameters turns out quite hard in practice,
since the behaviour is essentially contingent and most combinations
either lead to uninteresting trivial small graph chunks, or to
large, interconnected and exponentially expanding networks
... seeding happens at random points in the middle of the chain
... when combined with reduction, the resulting processing pattern
resembles the real processing pattern of media calcualtions
... 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.
- present the weight centres relative to overall level count
- detect sub-graphs and add statistics per subgraph
- include an evaluation for ''all nodes''
- include number of levels and subgraphs
- iterate over all nodes and classify them
- group per level
- book in per level statistics into the Indicator records
- close global averages
...just coded, not yet tested...
The graph will be used to generate a computational load
for testing the Scheduler; thus we need to compute some
statistical indicators to characterise this load.
As starting point sum counts and averages will be aggregated,
accounting for particular characterisation of nodes per level.
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
