Vertices representing variables (DfgVertexVar) and constants (DfgConst)
are very common (40-50% of all vertices created in some large designs),
and we also need to, or can treat them specially in algorithms. Keep
these as separate lists in DfgGraph for direct access to them. This
improve verilation speed.
Allow constant folding through adjacent nodes of all associative
operations, for example '((a & 2) & 3)' or '(3 & (2 & a))' can now be
folded into '(a & 2)' and '(2 & a)' respectively. Also improve speed of
making associative expression trees right leaning by using rotation of
the existing vertices whenever instead of allocation of new nodes.
Also added a testing only -fno-const-before-dfg option, as otherwise
V3Const eats up a lot of the simple inputs. A lot of the things V3Const
swallows in the simple cases can make it to DFG in complex cases, or DFG
itself can create them during optimization. In any case to save
complexity of testing DFG constant folding, we use this option to turn
off V3Const prior to the DFG passes in the relevant test.
Added a new data-flow graph (DFG) based combinational logic optimizer.
The capabilities of this covers a combination of V3Const and V3Gate, but
is also more capable of transforming combinational logic into simplified
forms and more.
This entail adding a new internal representation, `DfgGraph`, and
appropriate `astToDfg` and `dfgToAst` conversion functions. The graph
represents some of the combinational equations (~continuous assignments)
in a module, and for the duration of the DFG passes, it takes over the
role of AstModule. A bulk of the Dfg vertices represent expressions.
These vertex classes, and the corresponding conversions to/from AST are
mostly auto-generated by astgen, together with a DfgVVisitor that can be
used for dynamic dispatch based on vertex (operation) types.
The resulting combinational logic graph (a `DfgGraph`) is then optimized
in various ways. Currently we perform common sub-expression elimination,
variable inlining, and some specific peephole optimizations, but there
is scope for more optimizations in the future using the same
representation. The optimizer is run directly before and after inlining.
The pre inline pass can operate on smaller graphs and hence converges
faster, but still has a chance of substantially reducing the size of the
logic on some designs, making inlining both faster and less memory
intensive. The post inline pass can then optimize across the inlined
module boundaries. No optimization is performed across a module
boundary.
For debugging purposes, each peephole optimization can be disabled
individually via the -fno-dfg-peepnole-<OPT> option, where <OPT> is one
of the optimizations listed in V3DfgPeephole.h, for example
-fno-dfg-peephole-remove-not-not.
The peephole patterns currently implemented were mostly picked based on
the design that inspired this work, and on that design the optimizations
yields ~30% single threaded speedup, and ~50% speedup on 4 threads. As
you can imagine not having to haul around redundant combinational
networks in the rest of the compilation pipeline also helps with memory
consumption, and up to 30% peak memory usage of Verilator was observed
on the same design.
Gains on other arbitrary designs are smaller (and can be improved by
analyzing those designs). For example OpenTitan gains between 1-15%
speedup depending on build type.
