- Use modern C++
- Implement OrderLogicVertex->LogicMTask map with
OrderLogicVertex::userp(), insteas of std::unordered_map
- Simplify data structures
- Simplify code and assert properties
No functional change.
Refactor ProcessMoveBuildGraph utilizing the fact that OrderGraph is a
bipartite graph, also remove unnecessary unordered_map and distribute
variable domain map. No functional change.
Adds timing support to Verilator. It makes it possible to use delays,
event controls within processes (not just at the start), wait
statements, and forks.
Building a design with those constructs requires a compiler that
supports C++20 coroutines (GCC 10, Clang 5).
The basic idea is to have processes and tasks with delays/event controls
implemented as C++20 coroutines. This allows us to suspend and resume
them at any time.
There are five main runtime classes responsible for managing suspended
coroutines:
* `VlCoroutineHandle`, a wrapper over C++20's `std::coroutine_handle`
with move semantics and automatic cleanup.
* `VlDelayScheduler`, for coroutines suspended by delays. It resumes
them at a proper simulation time.
* `VlTriggerScheduler`, for coroutines suspended by event controls. It
resumes them if its corresponding trigger was set.
* `VlForkSync`, used for syncing `fork..join` and `fork..join_any`
blocks.
* `VlCoroutine`, the return type of all verilated coroutines. It allows
for suspending a stack of coroutines (normally, C++ coroutines are
stackless).
There is a new visitor in `V3Timing.cpp` which:
* scales delays according to the timescale,
* simplifies intra-assignment timing controls and net delays into
regular timing controls and assignments,
* simplifies wait statements into loops with event controls,
* marks processes and tasks with timing controls in them as
suspendable,
* creates delay, trigger scheduler, and fork sync variables,
* transforms timing controls and fork joins into C++ awaits
There are new functions in `V3SchedTiming.cpp` (used by `V3Sched.cpp`)
that integrate static scheduling with timing. This involves providing
external domains for variables, so that the necessary combinational
logic gets triggered after coroutine resumption, as well as statements
that need to be injected into the design eval function to perform this
resumption at the correct time.
There is also a function that transforms forked processes into separate
functions.
See the comments in `verilated_timing.h`, `verilated_timing.cpp`,
`V3Timing.cpp`, and `V3SchedTiming.cpp`, as well as the internals
documentation for more details.
Signed-off-by: Krzysztof Bieganski <kbieganski@antmicro.com>
Various optimizations to speed up MTasks coarsening (which is the long
pole in the multi-threaded scheduling of very large designs).
The biggest impact ones:
- Use efficient hand written Pairing Heaps for implementing priority
queues and the scoreboard, instead of the old SortByValueMap. This
helps us avoid having to sort a lot of merge candidates that we will
never actually consider and helps a lot in performance.
- Remove unnecessary associative containers and store data structures
(the heap nodes in particular) directly in the object they relate to.
This eliminates a huge amount of lookups and helps a lot in
performance.
- Distribute storage for SiblingMC instances into the LogicMTask
instances, and combine with the sibling maps. This again eliminates
hash table lookups and makes storage structures smaller.
- Remove some now bidirectional edge maps, keep only the forward map.
There are also some other smaller optimizations:
- Replaced more unnecessary dynamic_casts with static_casts
- Templated some functions/classes to reduce the number of static
branches in loops.
- Improves sorting of edges for sibling candidate creation
- Various micro-optimizations here and there
This speeds up MTask coarsening by 3.8x on a large design, which
translates to a 2.5x speedup of the ordering pass in multi-threaded
mode. (Combined with the earlier optimizations, ordering is now 3x
faster.)
Due to the elimination of a lot of the auxiliary data structures, and
ensuring a minimal size for the necessary ones, memory consumption of
the MTask coarsening is also reduced (measured up to 4.4x reduction
though the accuracy of this is low).
The algorithm is identical except for minor alterations of the order
some candidates are added or removed, this can cause perturbation in the
output due to tied scores being broken based on IDs.
Various optimizations to speed up MTasks coarsening (which is the long
pole in the multi-threaded scheduling of very large designs).
The biggest impact ones:
- Use efficient hand written Pairing Heaps for implementing priority
queues and the scoreboard, instead of the old SortByValueMap. This
helps us avoid having to sort a lot of merge candidates that we will
never actually consider and helps a lot in performance.
- Remove unnecessary associative containers and store data structures
(the heap nodes in particular) directly in the object they relate to.
This eliminates a huge amount of lookups and helps a lot in
performance.
- Distribute storage for SiblingMC instances into the LogicMTask
instances, and combine with the sibling maps. This again eliminates
hash table lookups and makes storage structures smaller.
- Remove some now bidirectional edge maps, keep only the forward map.
There are also some other smaller optimizations:
- Replaced more unnecessary dynamic_casts with static_casts
- Templated some functions/classes to reduce the number of static
branches in loops.
- Improves sorting of edges for sibling candidate creation
- Various micro-optimizations here and there
This speeds up MTask coarsening by 3.8x on a large design, which
translates to a 2.5x speedup of the ordering pass in multi-threaded
mode. (Combined with the earlier optimizations, ordering is now 3x
faster.)
Due to the elimination of a lot of the auxiliary data structures, and
ensuring a minimal size for the necessary ones, memory consumption of
the MTask coarsening is also reduced (measured up to 4.4x reduction
though the accuracy of this is low).
The algorithm is identical except for minor alterations of the order
some candidates are added or removed, this can cause perturbation in the
output due to tied scores being broken based on IDs.
While keeping the client code abstract in PartPropagateCp is nice for
testing, there is performance to be had removing the abstraction. As
this code dominates in scheduling large designs, we eliminate the
abstraction and re-work the testing to use the actual LogicMTask and
MTaskEdge graph types. No functional change intended.
Instead of deleting then re-allocating MTaskEdge instances when merging
two MTasks, just redirect the edged of the donor MTask to the recipient
MTask. This is both faster as it avoids an allocation and a deletion,
together with one update of the sibling maps, and also makes the
algorithm more stable due to MergeCandidate IDs being stable and
allocated up front for all MTaskEdges, before any SiblingMCs are
allocated.
Perturbations in output are expected as the IDs used to break ties
between merge candidates with equal costs are not updated when
redirecting an edge (on purpose). The relinking of only one end of the
graph edges also perturbs the order in which they are enumerated, which
does change candidate opportunities when the number of edges is larger
than PART_SIBLING_EDGE_LIMIT. Confirmed output is identical when
IDs are updated and edges are updated to appear in their original order.
The critical path propagation used to rely on a pointer comparison to
break equal scoring critical path updates. Use the corresponding mtask
ids instead, which is deterministic across invocations.
siblingPairFromRelatives gathers neighbours of a vertex, and sorts them.
It then takes the N best nodes, and creates sibling merge candidates
from them. We now use the unadjusted cost instead of the step cost of
the vertices when sorting. This is both faster as we need not do the
log-space rounding to compute stepCost, and will also make similar but
yet cheaper nodes appear closer to the front as we don't lose precision
in rounding, hence they are more likely to be entered as merge
candidates. Note that when creating the merge candidate, we still use
the stepCost, so it's purpose of reducing the propagation of critical
path updates is maintained in full. In summary, this should make both
Verilator and the generated model very slightly faster, at least in
theory, and I have observed minor improvement in places.
GraphStreamUnordered used to be GraphStream<std::less<const
V3GraphVertex*>>, but a lot of performance improvements can be had by a
specialized implementation, so added a highly optimized one. This helps
a lot with --debug-partition.
