This saves about 5% memory. V3AstUserAllocator is appropriate for most use
cases, performance is marginally up as we are mostly D-cache bound on
large designs.
Again --prof-exec have bit-rotted a little with all the recent changes
to the structure of the generated code. This patch contains a few
improvements:
- Repalce the eval/evl_loop begin/end events with generic
section_push/section_pop events, that can be arbitrarily sprinkled
into the generate code (so long as they are matched correctly) to
measure various sections. The report then contains a nested profile
of the sections, and the VCD trace shows the section names.
- Better handling of exec graphs
- Clearer overall statistics
Apart from the representational changes below, this patch renames
AstNodeMath to AstNodeExpr, and AstCMath to AstCExpr.
Now every expression (i.e.: those AstNodes that represent a [possibly
void] value, with value being interpreted in a very general sense) has
AstNodeExpr as a super class. This necessitates the introduction of an
AstStmtExpr, which represents an expression in statement position, e.g :
'foo();' would be represented as AstStmtExpr(AstCCall(foo)). In exchange
we can get rid of isStatement() in AstNodeStmt, which now really always
represent a statement
Peak memory consumption and verilation speed are not measurably changed.
Partial step towards #3420
- Rename `--dump-treei` option to `--dumpi-tree`, which itself is now a
special case of `--dumpi-<tag>` where tag can be a magic word, or a
filename
- Control dumping via static `dump*()` functions, analogous to `debug()`
- Make dumping independent of the value of `debug()` (so dumping always
works even without the debug flag)
- Add separate `--dumpi-graph` for dumping V3Graphs, which is again a
special case of `--dumpi-<tag>`
- Alias `--dump-<tag>` to `--dumpi-<tag> 3` as before
Introduce the @astgen directives parsed by astgen, currently used for
the generation child node (operand) accessors. Please see the updated
internal documentation for details.
Introduce the @astgen directives parsed by astgen, currently used for
the generation child node (operand) accessors. Please see the updated
internal documentation for details.
Small fixup patch so the 'ico' and 'act' scheduling sections could be
ordered as multi-threaded. However, we still only order these single
threaded at the moment (but switching them to multi-threaded now works).
Replace std::set<SiblingMC> with V3Lists to keep track of SiblingMCs
associated with MTasks, use a std::set<LogicMTask*> for ensuring
uniqueness. This yields a bit more speed in PartContraction.
- 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.
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.
Wilson Snyder
Geza Lore
github action
Mariusz Glebocki
Ryszard Rozak
Krzysztof Bieganski
Kamil Rakoczy
HungMingWu