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===================
Verilator Internals
===================

.. contents::
   :depth: 3

Introduction
============

This file discusses internal and programming details for Verilator. It's a
reference for developers and debugging problems.

See also the Verilator internals presentation at https://www.veripool.org.


Code Flows
==========


Verilator Flow
--------------

The main flow of Verilator can be followed by reading the Verilator.cpp
``process()`` function:

#. First, the files specified on the command line are read. Reading
   involves preprocessing, then lexical analysis with Flex and parsing with
   Bison. This produces an abstract syntax tree (AST) representation of the
   design, which is what is visible in the .tree files described below.

#. Verilator then makes a series of passes over the AST, progressively
   refining and optimizing it.

#. Cells in the AST first linked, which will read and parse additional
   files as above.

#. Functions, variable, and other references are linked to their
   definitions.

#. Parameters are resolved, and the design is elaborated.

#. Verilator then performs additional edits and optimizations on the
   hierarchical design. This includes coverage, assertions, X elimination,
   inlining, constant propagation, and dead code elimination.

#. References in the design are then pseudo-flattened. Each module's
   variables and functions get "Scope" references. A scope reference is an
   occurrence of that un-flattened variable in the flattened hierarchy. A
   module that occurs only once in the hierarchy will have a single scope
   and single VarScope for each variable. A module that occurs twice will
   have a scope for each occurrence, and two VarScopes for each variable.
   This allows optimizations to proceed across the flattened design while
   still preserving the hierarchy.

#. Additional edits and optimizations proceed on the pseudo-flat design.
   These include module references, function inlining, loop unrolling,
   variable lifetime analysis, lookup table creation, always splitting, and
   logic gate simplifications (pushing inverters, etc.).

#. Verilator orders the code. Best case, this results in a single "eval"
   function, which has all always statements flowing from top to bottom
   with no loops.

#. Verilator mostly removes the flattening, so that code may be shared
   between multiple invocations of the same module. It localizes variables,
   combines identical functions, expands macros to C primitives, adds
   branch prediction hints, and performs additional constant propagation.

#. Verilator finally writes the C++ modules.


Key Classes Used in the Verilator Flow
--------------------------------------


``AstNode``
~~~~~~~~~~~

The AST is represented at the top level by the class ``AstNode``. This
abstract class has derived classes for the individual components (e.g.
``AstGenerate`` for a generate block) or groups of components (e.g.
``AstNodeFTask`` for functions and tasks, which in turn has ``AstFunc`` and
``AstTask`` as derived classes). An important property of the ``AstNode``
type hierarchy is that all non-final subclasses of ``AstNode`` (i.e.: those
which themselves have subclasses) must be abstract as well, and be named
with the prefix ``AstNode*``. The ``astgen`` (see below) script relies on
this.

Each ``AstNode`` has pointers to up to four children, accessed by the
``op1p`` through ``op4p`` methods. These methods are then abstracted in a
specific Ast\* node class to a more specific name. For example, with the
``AstIf`` node (for ``if`` statements), ``thensp`` calls ``op2p`` to give
the pointer to the AST for the "then" block, while ``elsesp`` calls
``op3p`` to give the pointer to the AST for the "else" block, or NULL if
there is not one. These accessors are automatically generated by ``astgen``
after parsing the ``@astgen`` directives in the specific ``AstNode``
subclasses.

``AstNode`` has the concept of a next and previous AST - for example, the
next and previous statements in a block. Pointers to the AST for these
statements (if they exist) can be obtained using the ``back`` and ``next``
methods.

It is useful to remember that the derived class ``AstNetlist`` is at the
top of the tree, so checking for this class is the standard way to see if
you are at the top of the tree.

By convention, each function/method uses the variable ``nodep`` as a
pointer to the ``AstNode`` currently being processed.

There are notable sub-hierarchies of the ``AstNode`` sub-types, namely:

1. All AST nodes representing data types derive from ``AstNodeDType``.

2. All AST nodes representing expressions (i.e.: anything that stands for,
   or evaluates to a value) derive from ``AstNodeExpr``.


``VNVisitor``
~~~~~~~~~~~~~

The passes are implemented by AST visitor classes. These are implemented by
subclasses of the abstract class, ``VNVisitor``. Each pass creates an
instance of the visitor class, which in turn implements a method to perform
the pass.


``V3Graph``
~~~~~~~~~~~

A number of passes use graph algorithms, and the class ``V3Graph`` is
provided to represent those graphs. Graphs are directed, and algorithms are
provided to manipulate the graphs and output them in `GraphViz
<https://www.graphviz.org>`__ dot format. ``V3Graph.h`` provides
documentation of this class.


``V3GraphVertex``
~~~~~~~~~~~~~~~~~

``V3GraphVertex`` is the base class for vertices in a graph. Vertices have
an associated ``fanout``, ``color`` and ``rank``, which may be used in
algorithms for ordering the graph. A generic ``user``/``userp`` member
variable is also provided.

Virtual methods are provided to specify the name, color, shape, and style
to be used in dot output. Typically users provide derived classes from
``V3GraphVertex`` which will reimplement these methods.

Iterators are provided to access in and out edges. Typically these are used
in the form:

::

   for (V3GraphEdge *edgep = vertexp->inBeginp();
      edgep;
      edgep = edgep->inNextp()) {


``V3GraphEdge``
~~~~~~~~~~~~~~~

``V3GraphEdge`` is the base class for directed edges between pairs of
vertices. Edges have an associated ``weight`` and may also be made
``cutable``. A generic ``user``/``userp`` member variable is also provided.

Accessors, ``fromp`` and ``top`` return the "from" and "to" vertices
respectively.

Virtual methods are provided to specify the label, color, and style to be
used in dot output. Typically users provided derived classes from
``V3GraphEdge``, which will reimplement these methods.


``V3GraphAlg``
~~~~~~~~~~~~~~

This is the base class for graph algorithms. It implements a ``bool``
method, ``followEdge`` which algorithms can use to decide whether an edge
is followed. This method returns true if the graph edge has a weight
greater than one and a user function, ``edgeFuncp`` (supplied in the
constructor) returns ``true``.

A number of predefined derived algorithm classes and access methods are
provided and documented in ``V3GraphAlg.cpp``.


``DfgGraph``
~~~~~~~~~~~~

The data-flow graph-based combinational logic optimizer (DFG optimizer)
converts an ``AstModule`` into a ``DfgGraph``. The graph represents the
combinational equations (~continuous assignments) in the module, and for
the duration of the DFG passes, it takes over the role of the represented
``AstModule``. The ``DfgGraph`` keeps hold of the represented
``AstModule``, and the ``AstModule`` retains all other logic that is not
representable as a data-flow graph. At the end of optimization, the
combinational logic represented by the ``DfgGraph`` is converted back into
AST form and is re-inserted into the corresponding ``AstModule``. The
``DfgGraph`` is distinct from ``V3Graph`` for efficiency and other
desirable properties which make writing DFG passes easier.


``DfgVertex``
~~~~~~~~~~~~~

The ``DfgGraph`` represents combinational logic equations as a graph of
``DfgVertex`` vertices. Each sub-class of ``DfgVertex`` corresponds to an
expression (a sub-class of ``AstNodeExpr``), a constant, or a variable
reference. LValues and RValues referencing the same storage location are
represented by the same ``DfgVertex``. Consumers of such vertices read as
the LValue, writers of such vertices write the RValue. The bulk of the
final ``DfgVertex`` sub-classes are generated by ``astgen`` from the
corresponding ``AstNode`` definitions.


Scheduling
----------

Verilator implements the Active and NBA regions of the SystemVerilog
scheduling model as described in IEEE 1800-2023 chapter 4, and in
particular sections 4.5 and Figure 4.1. The static (Verilation time)
scheduling of SystemVerilog processes is performed by code in the
``V3Sched`` namespace. The single entry point to the scheduling algorithm
is ``V3Sched::schedule``. Some preparatory transformations important for
scheduling are also performed in ``V3Active`` and ``V3ActiveTop``.
High-level evaluation functions are constructed by ``V3Order``, which
``V3Sched`` invokes on subsets of the logic in the design.

Scheduling deals with the problem of evaluating 'logic' in the correct
order and the correct number of times in order to compute the correct state
of the SystemVerilog program. Throughout this section, we use the term
'logic' to refer to all SystemVerilog constructs that describe the
evolution of the state of the program. In particular, all SystemVerilog
processes and continuous assignments are considered 'logic', but not for
example variable definitions without initialization or other miscellaneous
constructs.


Classes of logic
~~~~~~~~~~~~~~~~

The first step in the scheduling algorithm is to gather all the logic
present in the design, and classify it based on the conditions under which
the logic needs to be evaluated.

The classes of logic we distinguish between are:

- SystemVerilog ``initial`` processes, that need to be executed once at
  startup.

- Static variable initializers. These are a separate class as they need to
  be executed before ``initial`` processes.

- SystemVerilog ``final`` processes.

- Combinational logic. Any process or construct that has an implicit
  sensitivity list with no explicit sensitivities is considered
  'combinational' logic. This includes among other things, ``always @*``
  and ``always_comb`` processes, and continuous assignments. Verilator also
  converts some other ``always`` processes to combinational logic in
  ``V3Active`` as described below.

- Clocked logic. Any process or construct that has an explicit sensitivity
  list, with no implicit sensitivities, is considered 'clocked' (or
  'sequential') logic. This includes, among other things ``always`` and
  ``always_ff`` processes with an explicit sensitivity list.

Note that the distinction between clocked logic and combinational logic is
only important for the scheduling algorithm within Verilator as we handle
the two classes differently. It is possible to convert clocked logic into
combinational logic if the explicit sensitivity list of the clocked logic
is the same as the implicit sensitivity list of the equivalent
combinational logic would be. The canonical examples are: ``always @(a) x =
a;``, which is considered to be clocked logic by Verilator, and the
equivalent ``assign x = a;``, which is considered to be combinational
logic. ``V3Active`` in fact converts all clocked logic to combinational
logic whenever possible, as this provides advantages for scheduling as
described below.

There is also a 'hybrid' logic class, which has both explicit and implicit
sensitivities. This kind of logic does not arise from a SystemVerilog
construct, but is created during scheduling to break combinational cycles.
Details of this process and the hybrid logic class are described below.


Scheduling of simple classes
~~~~~~~~~~~~~~~~~~~~~~~~~~~~

SystemVerilog ``initial`` and ``final`` blocks can be scheduled (executed)
in an arbitrary order.

Static variable initializers need to be executed in source code order in
case there is a dependency between initializers, but the ordering of static
variable initialization is otherwise not defined by the SystemVerilog
standard (particularly, in the presence of hierarchical references in
static variable initializers).

The scheduling algorithm handles all three of these classes the same way
and schedules the logic in these classes in source code order. This step
yields the ``_eval_static``, ``_eval_initial`` and ``_eval_final``
functions which execute the corresponding logic constructs.


Scheduling of clocked and combinational logic
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

For performance, clocked and combinational logic needs to be ordered.
Conceptually this minimizes the iterations through the evaluation loop
presented in the reference algorithm in the SystemVerilog standard (IEEE
1800-2023 section 4.5), by evaluating logic constructs in data-flow order.
Without going into a lot of detail here, accept that well thought out
ordering is crucial to good simulation performance, and also enables
further optimizations later on.

At the highest level, ordering is performed by ``V3Order::order``, which is
invoked by ``V3Sched::schedule`` on various subsets of the combinational
and clocked logic as described below. The important thing to highlight now
is that ``V3Order::order`` operates by assuming that the state of all
variables driven by combinational logic is consistent with that
combinational logic. While this might seem subtle, it is very important, so
here is an example:

.. code-block:: sv

   always_comb d = q + 2;
   always @(posedge clock) q <= d;

During ordering, ``V3Order`` will assume that ``d`` equals ``q + 2`` at the
beginning of an evaluation step. As a result it will order the clocked
logic first, and all downstream combinational logic (like the assignment to
``d``) will execute after the clocked logic that drives inputs to the
combinational logic, in data-flow (or dependency) order. At the end of the
evaluation step, this ordering restores the invariant that variables driven
by combinational logic are consistent with that combinational logic (i.e.,
the circuit is in a settled/steady state).

One of the most important optimizations for performance is to only evaluate
combinational logic, if its inputs might have changed. For example, there
is no point in evaluating the above assignment to ``d`` on a negative edge
of the clock signal. Verilator does this by pushing the combinational logic
into the same (possibly multiple) event domains as the logic driving the
inputs to that combinational logic, and only evaluating the combinational
logic if at least one driving domain has been triggered. The impact of this
activity gating is very high (observed 100x slowdown on large designs when
turning it off), it is the reason we prefer to convert clocked logic to
combinational logic in ``V3Active`` whenever possible.

The ordering procedure described above works straightforward unless there
are combinational logic constructs that are circularly dependent (a.k.a.:
the UNOPTFLAT warning). Combinational scheduling loops can arise in sound
(realizable) circuits as Verilator considers each SystemVerilog process as
a unit of scheduling (albeit we do try to split processes into smaller ones
to avoid this circularity problem whenever possible, this is not always
possible).


Breaking combinational loops
~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Combinational loops are broken by the introduction of instances of the
'hybrid' logic class. As described in the previous section, combinational
loops require iteration until the logic is settled, in order to restore the
invariant that combinationally driven signals are consistent with the
combinational logic.

To achieve this, ``V3Sched::schedule`` calls ``V3Sched::breakCycles``,
which builds a dependency graph of all combinational logic in the design,
and then breaks all combinational cycles by converting all combinational
logic that consumes a variable driven via a 'back-edge' into hybrid logic.
Here 'back-edge' just means a graph edge that points from a higher-rank
vertex to a lower rank vertex in some consistent ranking of the directed
graph. Variables driven via a back-edge in the dependency graph are marked,
and all combinational logic that depends on such variables is converted
into hybrid logic, with the back-edge driven variables listed as explicit
'changed' sensitivities.

Hybrid logic is handled by ``V3Order`` mostly in the same way as
combinational logic, with two exceptions:

- Explicit sensitivities of hybrid logic are ignored for the purposes of
  data-flow ordering with respect to other combinational or hybrid logic.
  I.e.: an explicit sensitivity suppresses the implicit sensitivity on the
  same variable. This could also be interpreted as ordering the hybrid
  logic as if all variables listed as explicit sensitivities were
  substituted as constants with their current values.

- The explicit sensitivities are included as an additional driving domain
  of the logic, and also cause evaluation when triggered.

This means that hybrid logic is evaluated when either any of its implicit
sensitivities might have been updated (the same way as combinational logic,
by pushing it into the domains that write those variables), or if any of
its explicit sensitivities are triggered.

The effect of this transformation is that ``V3Order`` can proceed as if
there are no combinational cycles (or alternatively, under the assumption
that the back-edge-driven variables don't change during one evaluation
pass). The evaluation loop invoking the ordered code, will then re-invoke
it on a follow on iteration, if any of the explicit sensitivities of hybrid
logic have actually changed due to the previous invocation, iterating until
all the combinational (including hybrid) logic have settled.

One might wonder if there can be a race condition between clocked logic
triggered due to a combinational signal change from the previous evaluation
pass, and a combinational loop settling due to hybrid logic, if the clocked
logic reads the not yet settled combinationally driven signal. Such a race
is indeed possible, but our evaluation is consistent with the SystemVerilog
scheduling semantics (IEEE 1800-2023 chapter 4), and therefore any program
that exhibits such a race has non-deterministic behavior according to the
SystemVerilog semantics, so we accept this.


Settling combinational logic after initialization
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

At the beginning of simulation, once static initializer and ``initial``
blocks have been executed, we need to evaluate all combinational logic, in
order to restore the invariant utilized by ``V3Order`` that the state of
all combinationally driven variables are consistent with the combinational
logic.

To achieve this, we invoke ``V3Order::order`` on all of the combinational
and hybrid logic, and iterate the resulting evaluation function until no
more hybrid logic is triggered. This yields the `_eval_settle` function,
which is invoked at the beginning of simulation after the `_eval_initial`.


Partitioning logic for correct NBA updates
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

``V3Order`` can order logic corresponding to non-blocking assignments
(NBAs) to yield correct simulation results, as long as all the sensitivity
expressions of clocked logic triggered in the Active scheduling region of
the current time step are known up front. I.e., the ordering of NBA updates
is only correct if derived clocks that are computed in an Active region
update (that is, via a blocking or continuous assignment) are known up
front.

We can ensure this by partitioning the logic into two regions. Note these
regions are a concept of the Verilator scheduling algorithm, and they do
not directly correspond to the similarly named SystemVerilog scheduling
regions as defined in the standard:

- All logic (clocked, combinational and hybrid) that transitively feeds
  into, or drives via a non-blocking or continuous assignments (or via any
  update that SystemVerilog executes in the Active scheduling region), a
  variable that is used in the explicit sensitivity list of some clocked or
  hybrid logic, is assigned to the 'act' region.

- All other logic is assigned to the 'nba' region.

For completeness, note that a subset of the 'act' region logic,
specifically, the logic related to the pre-assignments of NBA updates
(i.e., AstAlwaysPre nodes), is handled separately, but is executed as part
of the 'act' region.

Also note that all logic representing the committing of an NBA (i.e.,
Ast*Post) nodes) will be in the 'nba' region. This means that the
evaluation of the 'act' region logic will not commit any NBA updates. As a
result, the 'act' region logic can be iterated to compute all derived clock
signals up front.

The correspondence between the SystemVerilog Active and NBA scheduling
regions, and the internal 'act' and 'nba' regions, is that 'act' contains
all Active region logic that can compute a clock signal, while 'nba'
contains all other Active and NBA region logic. For example, if the only
clocks in the design are top-level inputs, then 'act' will be empty, and
'nba' will contain the whole of the design.

The partitioning described above is performed by ``V3Sched::partition``.


Replication of combinational logic
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

We will separately invoke ``V3Order::order`` on the 'act' and 'nba' region
logic.

Combinational logic that reads variables driven from both 'act' and 'nba'
region logic has the problem of needing to be reevaluated even if only one
of the regions updates an input variable. We could pass additional trigger
expressions between the regions to make sure combinational logic is always
reevaluated, or we can replicate combinational logic that is driven from
multiple regions, by copying it into each region that drives it.
Experiments show this simple replication works well performance-wise (and
notably ``V3Combine`` is good at combining the replicated code), so this is
what we do in ``V3Sched::replicateLogic``.

In ``V3Sched::replicateLogic``, in addition to replicating logic into the
'act' and 'nba' regions, we also replicate combinational (and hybrid) logic
that depends on top level inputs. These become a separate 'ico' region
(Input Combinational logic), which we will always evaluate at the beginning
of a time-step to ensure the combinational invariant holds even if input
signals have changed. Note that this eliminates the need of changing data
and clock signals on separate evaluations, as was necessary with earlier
versions of Verilator).


Constructing the top level `_eval` function
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

To construct the top level `_eval` function, which updates the state of the
circuit to the end of the current time step, we invoke ``V3Order::order``
separately on the 'ico', 'act' and 'nba' logic, which yields the
`_eval_ico`, `_eval_act`, and `_eval_nba` functions. We then put these all
together with the corresponding functions that compute the respective
trigger expressions into the top level `_eval` function, which on the high
level has the form:

.. code-block:: C++

   void _eval() {
      // Update combinational logic dependent on top level inputs ('ico' region)
      while (true) {
        _eval__triggers__ico();
        // If no 'ico' region trigger is active
        if (!ico_triggers.any()) break;
        _eval_ico();
      }

      // Iterate 'act' and 'nba' regions together
      while (true) {

        // Iterate 'act' region, this computes all derived clocks updaed in the
        // Active scheduling region, but does not commit any NBAs that executed
        // in 'act' region logic.
        while (true) {
          _eval__triggers__act();
          // If no 'act' region trigger is active
          if (!act_triggers.any()) break;
          // Remember what 'act' triggers were active, 'nba' uses the same
          latch_act_triggers_for_nba();
          _eval_act();
        }

        // If no 'nba' region trigger is active
        if (!nba_triggers.any()) break;

        // Evaluate all other Active region logic, and commit NBAs
        _eval_nba();
      }
   }


Timing
------

Timing support in Verilator utilizes C++ coroutines, which is a new feature
in C++20. The basic idea is to represent processes and tasks that await a
certain event or simulation time as coroutines. These coroutines get
suspended at the await, and resumed whenever the triggering event occurs,
or at the expected simulation time.

There are several runtime classes used for managing such coroutines defined
in ``verilated_timing.h`` and ``verilated_timing.cpp``.

``VlCoroutineHandle``
~~~~~~~~~~~~~~~~~~~~~

A thin wrapper around an ``std::coroutine_handle<>``. It forces move
semantics, destroys the coroutine if it remains suspended at the end of the
design's lifetime, and prevents multiple ``resume`` calls in the case of
``fork..join_any``.

``VlCoroutine``
~~~~~~~~~~~~~~~

Return value of all coroutines. Together with the promise type contained
within, it allows for chaining coroutines - resuming coroutines from up the
call stack. The calling coroutine's handle is saved in the promise object
as a continuation, that is, the coroutine that must be resumed after the
promise's coroutine finishes. This is necessary as C++ coroutines are
stackless, meaning each one is suspended independently of others in the
call graph.

``VlDelayScheduler``
~~~~~~~~~~~~~~~~~~~~

This class manages processes suspended by delays. There is one instance of
this class per design. Coroutines ``co_await`` this object's ``delay``
function. Internally, they are stored in a heap structure sorted by
simulation time in ascending order. When ``resume`` is called on the delay
scheduler, all coroutines awaiting the current simulation time are resumed.
The current simulation time is retrieved from a ``VerilatedContext``
object.

``VlTriggerScheduler``
~~~~~~~~~~~~~~~~~~~~~~

This class manages processes that await events (triggers). There is one
such object per each trigger awaited by coroutines. Coroutines ``co_await``
this object's ``trigger`` function. They are stored in three stages -
`awaiting`, `fired` and `toResume`. First, they land in the `awaiting`
stage, and cannot be resumed. The ``ready`` function moves all coroutines
from the `awaiting` stage into the `fired` stage. The ``moveToResumeQueue``
function moves `fired` coroutines into `toResume`. Finally, function
`resume` resumes all coroutines from the `toResume` stage.

This split is done to avoid self-triggering, triggering coroutines multiple
times and triggering coroutines in the same iteration they were suspended.
See the `Scheduling with timing` section for details on how this is used.

``VlDynamicTriggerScheduler``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Like ``VlTriggerScheduler``, ``VlDynamicTriggerScheduler`` manages
processes that await triggers. However, it does not rely on triggers
evaluated externally by the 'act' trigger eval function. Instead, it is
also responsible for trigger evaluation. Coroutines that make use of this
scheduler must adhere to a certain procedure:

::

   __Vtrigger = 0;
   <locals and inits required for trigger eval>
   while (!__Vtrigger) {
       co_await __VdynSched.evaluation();
       <pre updates>;
       __Vtrigger = <trigger eval>;
       [optionally] co_await __VdynSched.postUpdate();
       <post updates>;
   }
   co_await __VdynSched.resumption();

The coroutines get resumed at trigger evaluation time, evaluate their local
triggers, optionally await the post update step, and if the trigger is set,
await proper resumption in the 'act' eval step.

``VlForkSync``
~~~~~~~~~~~~~~

Used for synchronizing ``fork..join`` and ``fork..join_any``. Forking
coroutines ``co_await`` its ``join`` function, and forked ones call
``done`` when they're finished. Once the required number of coroutines (set
using ``setCounter``) finish execution, the forking coroutine is resumed.

``VlForever``
~~~~~~~~~~~~~

A small utility awaitable type. It allows for blocking a coroutine forever.
It is currently only used for ``wait`` statements that await a constant
false condition. See the `Timing Pass` section for more details.

Timing Pass
~~~~~~~~~~~

There are two visitors in ``V3Timing.cpp``.

The first one, ``TimingSuspendableVisitor``, does not perform any AST
transformations. It is responsible for marking processes and C++ functions
that contain timing controls as suspendable. Processes that call
suspendable functions are also marked as suspendable. Functions that call,
are overridden by, or override suspendable functions are marked as
suspendable as well.

The visitor keeps a dependency graph of functions and processes to handle
such cases. A function or process is dependent on a function if it calls
it. A virtual class method is dependent on another class method if it calls
it, overrides it, or is overriden by it.

The second visitor in ``V3Timing.cpp``, ``TimingControlVisitor``, uses the
information provided by ``TimingSuspendableVisitor`` and transforms each
timing control into a ``co_await``.

* event controls are turned into ``co_await`` on a trigger scheduler's
  ``trigger`` method. The awaited trigger scheduler is the one
  corresponding to the sentree referenced by the event control. This
  sentree is also referenced by the ``AstCAwait`` node, to be used later by
  the static scheduling code.

* if an event control waits on a local variable or class member, it uses a
  local trigger which it evaluates inline. It awaits a dynamic trigger
  scheduler multiple times: for trigger evaluation, updates, and
  resumption. The dynamic trigger scheduler is responsible for resuming the
  coroutine at the correct point of evaluation.

* delays are turned into ``co_await`` on a delay scheduler's ``delay``
  method. The created ``AstCAwait`` nodes also reference a special sentree
  related to delays, to be used later by the static scheduling code.

* ``join`` and ``join_any`` are turned into ``co_await`` on a
  ``VlForkSync``'s ``join`` method. Each forked process gets a
  ``VlForkSync::done`` call at the end.

Assignments with intra-assignment timing controls are simplified into
assignments after those timing controls, with the LHS and RHS values
evaluated before them and stored in temporary variables.

``wait`` statements are transformed into while loops that check the
condition and then await changes in variables used in the condition. If the
condition is always false, the ``wait`` statement is replaced by a
``co_await`` on a ``VlForever``. This is done instead of a return in case
the ``wait`` is deep in a call stack (otherwise, the coroutine's caller
would continue execution).

Each sub-statement of a ``fork`` is put in an ``AstBegin`` node for easier
grouping. In a later step, each of these gets transformed into a new,
separate function. See the `Forks` section for more detail.

Suspendable functions get the return type of ``VlCoroutine``, which makes
them coroutines. Later, during ``V3Sched``, suspendable processes are also
transformed into coroutines.


Scheduling with timing
~~~~~~~~~~~~~~~~~~~~~~

Timing features in Verilator are built on top of the static scheduler.
Triggers are used for determining which delay or trigger schedulers should
resume. A special trigger is used for the delay scheduler. This trigger is
set if there are any coroutines awaiting the current simulation time
(``VlDelayScheduler::awaitingCurrentTime()``).

All triggers used by a suspendable process are mapped to variables written
in that process. When ordering code using ``V3Order``, these triggers are
provided as external domains of these variables. This ensures that the
necessary combinational logic is triggered after a coroutine resumption.

Every call to a `VlTriggerScheduler`'s `trigger()` method is preempt by a
call to a proper `__VbeforeTrig` function which evaluates all the necessary
triggers so, the information about order of suspension/resumption is not
lost. The triggers necessary to evaluate are ones dependent on the same
events as the `trigger()` - e.g.: if `triggers()` awaits for event `a` or
`b`, then every trigger that depends on any of those shall be evaluated. If
they wouldn't be evaluated and next coroutine after resumption would fire
the event `a` then it is impossible to get to know whether await or fire on
event `a` was called first - which is necessary to know.

There are two functions for managing timing logic called by ``_eval()``:

* ``_timing_ready()``, which commits all coroutines whose triggers were
  not set in the current iteration,
* ``_timing_resume()``, which calls `resume()` on all trigger and delay
  schedulers whose triggers were set in the current iteration.

Thanks to this separation a coroutine:

* awaiting a trigger cannot be suspended and resumed in the same iteration
  (``test_regress/t/t_timing_eval_act.v``) - which is necessary to make
  Verilator more predictable; this is the reason for introduction of 3rd
  stage in `VlTriggerScheduler` and thanks to this it is guaranteed that
  downstream logic will be evaluated before resumption (assuming that the
  coroutine wasn't already triggered in previous iteration);
* cannot be resumed before it is suspended -
  ``test_regress/t/t_event_control_double_excessive.v``;
* firing cannot cannot be lost
  (``test_regress/t/t_event_control_double_lost.v``) - which is possible when
  triggers are not evaluated right before awaiting.

All coroutines are committed and resumed in the 'act' eval loop. With
timing features enabled, the ``_eval()`` function takes this form:

::

   void _eval() {
     while (true) {
       _eval__triggers__ico();
       if (!ico_triggers.any()) break;
       _eval_ico();
     }

     while (true) {
       while (true) {
         _eval__triggers__act();

         // Commit all non-triggered coroutines
         _timing_commit();

         if (!act_triggers.any()) break;
         latch_act_triggers_for_nba();

         // Resume all triggered coroutines
         _timing_resume();

         _eval_act();
       }
       if (!nba_triggers.any()) break;
       _eval_nba();
     }
   }

Forks
~~~~~

After the scheduling step, forks sub-statements are transformed into
separate functions, and these functions are called in place of the
sub-statements. These calls must be without ``co_await``, so that
suspension of a forked process doesn't suspend the forking process.

In forked processes, references to local variables are only allowed in
``fork..join``, as this is the only case that ensures the lifetime of these
locals are at least as long as the execution of the forked processes.


Multithreaded Mode
------------------

In ``--threads`` mode, the frontend of the Verilator pipeline is the same
as serial mode, up until V3Order.

``V3Order`` builds a fine-grained, statement-level dependency graph that
governs the ordering of code within a single ``eval()`` call. In serial
mode, that dependency graph is used to order all statements into a total
serial order. In parallel mode, the same dependency graph is the starting
point for a partitioner (``V3Partition``).

The partitioner's goal is to coarsen the fine-grained graph into a coarser
graph, while maintaining as much available parallelism as possible. Often
the partitioner can transform an input graph with millions of nodes into a
coarsened execution graph with a few dozen nodes, while maintaining enough
parallelism to take advantage of a modern multicore CPU. Runtime
synchronization cost is reasonable with so few nodes.


Partitioning
~~~~~~~~~~~~

Our partitioner is similar to the one Vivek Sarkar described in his 1989
paper *Partitioning and Scheduling Parallel Programs for Multiprocessors*.

Let's define some terms:


Par Factor
~~~~~~~~~~

The available parallelism or "par-factor" of a DAG is the total cost to
execute all nodes, divided by the cost to execute the longest critical path
through the graph. This is the speedup you would get from running the graph
in parallel, if given infinite CPU cores available and communication and
synchronization is zero.


Macro Task
~~~~~~~~~~

When the partitioner coarsens the graph, it combines nodes together. Each
fine-grained node represents an atomic "task"; combined nodes in the
coarsened graph are "macro-tasks". This term comes from Sarkar. Each
macro-task executes from start to end on one processor, without any
synchronization to any other macro-task during its execution.
(Synchronization only happens before the macro-task begins or after it
ends.)


Edge Contraction
~~~~~~~~~~~~~~~~

Verilator's partitioner, like Sarkar's, primarily relies on "edge
contraction" to coarsen the graph. It starts with one macro-task per atomic
task and iteratively combines pairs of edge-connected macro-tasks.


Local Critical Path
~~~~~~~~~~~~~~~~~~~

Each node in the graph has a "local" critical path. That's the critical
path from the start of the graph to the start of the node, plus the node's
cost, plus the critical path from the end of the node to the end of the
graph.

Sarkar calls out an important trade-off: coarsening the graph reduces
runtime synchronization overhead among the macro-tasks, but it tends to
increase the critical path through the graph and thus reduces par-factor.

Sarkar's partitioner, and ours, chooses pairs of macro-tasks to merge such
that the growth in critical path is minimized. Each candidate merge would
result in a new node, which would have some local critical path. We choose
the candidate that would produce the shortest local critical path. Repeat
until par-factor falls to a target threshold. It's a greedy algorithm, and
it's not guaranteed to produce the best partition (which Sarkar proves is
NP-hard).


Estimating Logic Costs
~~~~~~~~~~~~~~~~~~~~~~

To compute the cost of any given path through the graph, Verilator
estimates an execution cost for each task. Each macro-task has an execution
cost which is the sum of its tasks' costs. We assume that communication
overhead and synchronization overhead are zero, so the cost of any given
path through the graph is the sum of macro-task execution costs. Sarkar
does almost the same thing, except that he has nonzero estimates for
synchronization costs.

Verilator's cost estimates are assigned by ``InstrCountVisitor``. This
class is perhaps the most fragile piece of the multithread implementation.
It's easy to have a bug where you count something cheap (e.g. accessing one
element of a huge array) as if it were expensive (eg. by counting it as if
it were an access to the entire array.) Even without such gross bugs, the
estimates this produce are only loosely predictive of actual runtime cost.
Multithread performance would be better with better runtime costs
estimates. This is an area to improve.


Scheduling Macro-Tasks at Runtime
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

After coarsening the graph, we must schedule the macro-tasks for runtime.
Sarkar describes two options: you can dynamically schedule tasks at
runtime, with a runtime graph follower. Sarkar calls this the
"macro-dataflow model." Verilator does not support this; early experiments
with this approach had poor performance.

The other option is to statically assign macro-tasks to threads, with each
thread running its macro-tasks in a static order. Sarkar describes this in
Chapter 5. Verilator takes this static approach. The only dynamic aspect is
that each macro task may block before starting, to wait until its
prerequisites on other threads have finished.

The synchronization cost is cheap if the prereqs are done. If they're not,
fragmentation (idle CPU cores waiting) is possible. This is the major
source of overhead in this approach. The ``--prof-exec`` switch and the
``verilator_gantt`` script can visualize the time lost to such
fragmentation.


Locating Variables for Best Spatial Locality
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

After scheduling all code, we attempt to locate variables in memory, such
that variables accessed by a single macro-task are close together in
memory. This provides "spatial locality" - when we pull in a 64-byte cache
line to access a 2-byte variable, we want the other 62 bytes to be ones
we'll also likely access soon, for best cache performance.

This is critical for performance. It should allow Verilator to scale to
very large models. We don't rely on our working set fitting in any CPU
cache; instead we essentially "stream" data into caches from memory. It's
not literally streaming, where the address increases monotonically, but it
should have similar performance characteristics, so long as each
macro-task's dataset fits in one core's local caches.

To achieve spatial locality, we tag each variable with the set of
macro-tasks that access it. Let's call this set the "footprint" of that
variable. The variables in a given module have a set of footprints. We can
order those footprints to minimize the distance between them (distance is
the number of macro-tasks that are different across any two footprints) and
then emit all variables into the struct in ordered-footprint order.

The footprint ordering is literally the traveling salesman problem, and we
use a TSP-approximation algorithm to get close to an optimal sort.

This is an old idea. Simulators designed at DEC in the early 1990s used
similar techniques to optimize both single-thread and multithread modes.
(Verilator does not optimize variable placement for spatial locality in
serial mode; that is a possible area for improvement.)


Improving Multithreaded Performance Further (a TODO list)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~


Wave Scheduling
+++++++++++++++

To allow the Verilated model to run in parallel with the testbench, it
might be nice to support "wave" scheduling, in which work on a cycle begins
before ``eval()`` is called or continues after ``eval()`` returns. For now,
all work on a cycle happens during the ``eval()`` call, leaving Verilator's
threads idle while the testbench (everything outside ``eval()``) is
working. This would involve fundamental changes within the partitioner,
however, it's probably the best bet for hiding testbench latency.


Efficient Dynamic Scheduling
++++++++++++++++++++++++++++

To scale to more than a few threads, we may revisit a fully dynamic
scheduler. For large (>16 core) systems, it might make sense to dedicate an
entire core to scheduling, so that scheduler data structures would fit in
its L1 cache and thus the cost of traversing priority-ordered ready lists
would not be prohibitive.


Static Scheduling with Runtime Repack
+++++++++++++++++++++++++++++++++++++

We could modify the static scheduling approach by gathering actual
macro-task execution times at run time, and dynamically re-packing the
macro-tasks into the threads also at run time. Say, re-pack once every
10,000 cycles or something. This has the potential to do better than our
static estimates about macro-task run times. It could potentially react to
CPU cores that aren't performing equally, due to NUMA or thermal throttling
or nonuniform competing memory traffic or whatever.


Clock Domain Balancing
++++++++++++++++++++++

Right now Verilator makes no attempt to balance clock domains across
macro-tasks. For a multi-domain model, that could lead to bad gantt chart
fragmentation. This could be improved if it's a real problem in practice.


Other Forms of MTask Balancing
++++++++++++++++++++++++++++++

The largest source of runtime overhead is idle CPUs, which happens due to
variance between our predicted runtime for each MTask and its actual
runtime. That variance is magnified if MTasks are homogeneous, containing
similar repeating logic which was generally close together in source code
and which is still packed together even after going through Verilator's
digestive tract.

If Verilator could avoid doing that, and instead would take source logic
that was close together and distribute it across MTasks, that would
increase the diversity of any given MTask, and this should reduce variance
in the cost estimates.

One way to do that might be to make various "tie breaker" comparison
routines in the sources to rely more heavily on randomness, and generally
try harder not to keep input nodes together when we have the option to
scramble things.

Profile-guided optimization make this a bit better, by adjusting mtask
scheduling, but this does not yet guide the packing into mtasks.


Performance Regression
++++++++++++++++++++++

It would be nice if we had a regression of large designs, with some
diversity of design styles, to test on both single- and multithreaded
modes. This would help to avoid performance regressions, and also to
evaluate the optimizations while minimizing the impact of parasitic noise.


Per-Instance Classes
++++++++++++++++++++

If we have multiple instances of the same module, and they partition
differently (likely; we make no attempt to partition them the same), then
the variable sort will be suboptimal for either instance. A possible
improvement would be to emit an unique class for each instance of a module,
and sort its variables optimally for that instance's code stream.


Verilated Flow
--------------

The evaluation loop outputted by Verilator is designed to allow a single
function to perform evaluation under most situations.

On the first evaluation, the Verilated code calls initial blocks, and then
"settles" the modules, by evaluating functions (from always statements)
until all signals are stable.

On other evaluations, the Verilated code detects what input signals have
changes. If any are clocks, it calls the appropriate sequential functions
(from ``always @ posedge`` statements). Interspersed with sequential
functions, it calls combo functions (from ``always @*``). After this is
complete, it detects any changes due to combo loops or internally generated
clocks, and if one is found must reevaluate the model again.

For SystemC code, the ``eval()`` function is wrapped in a SystemC
``SC_METHOD``, sensitive to all inputs. (Ideally, it would only be
sensitive to clocks and combo inputs, but tracing requires all signals to
cause evaluation, and the performance difference is small.)

If tracing is enabled, a callback examines all variables in the design for
changes, and writes the trace for each change. To accelerate this process,
the evaluation process records a bitmask of variables that might have
changed; if clear, checking those signals for changes may be skipped.


Constrained randomization
-------------------------

Because general constrained randomization is a co-NP-hard problem, not all
cases are implemented in Verilator, and an external specialized SMT solver
is used for any non-obvious ones.

The ``randomize()`` method spawns an SMT solver in a sub-process. Then the
solver gets a setup query, then the definition of variables, then all the
constraints (SMT assertions) about the variables. Since the solver has no
information about the class' PRNG state, if the problem is satisfiable, the
solution space is further constrained by adding extra random constraints,
and querying the values satisfying the problem statement. The constraint is
currently constructed as fixing a simple xor of randomly chosen bits of the
variables being randomized.

The runtime classes used for handling the randomization are defined in
``verilated_random.h`` and ``verilated_random.cpp``.


``VlSubprocess``
~~~~~~~~~~~~~~~~

Subprocess handle, responsible for keeping track of the resources like
child PID, read and write file descriptors, and presenting them as a C++
iostream.


``VlRandomizer``
~~~~~~~~~~~~~~~~

Randomizer class, responsible for keeping track of variables and
constraints, and communicating with the solver subprocess.

The solver gets the constraints in `SMT-LIB2
<https://smtlib.cs.uiowa.edu/>`__ textual format in the following syntax:

::

   (set-info :smt-lib-version 2.0)
   (set-option :produce-models true)
   (set-logic QF_BV)

   (declare-fun v () (_ BitVec 16))
   (declare-fun w () (_ BitVec 64))
   (declare-fun x () (_ BitVec 48))
   (declare-fun z () (_ BitVec 24))
   (declare-fun t () (_ BitVec 23))
   (assert (or (= v #x0003) (= v #x0008)))
   (assert (= w #x0000000000000009))
   (assert (or (or (= x #x000000000001) (= x #x000000000002)) (or (= x #x000000000004) (= x #x000000000009))))
   (assert (bvult ((_ zero_extend 8) z) #x00000015))
   (assert (bvugt ((_ zero_extend 8) z) #x0000000d))

   (check-sat)

The solver responds with either ``sat`` or ``unsat``. Then the initial
solution is queried with:

::

   (get-value (v w x z t ))

The solver then responds with e.g.:

::

   ((v #x0008)
    (w #x0000000000000005)
    (x #x000000000002)
    (z #x000010)
    (t #b00000000000000000000000))

And then a follow-up query (or a series thereof) is asked, and the solver
gets reset, so that it can be reused by subsequent randomization attempts:

::

   (assert (= (bvxor (bvxor <...> (bvxor ((_ extract 21 21) z) ((_ extract 39 39) x)) ((_ extract 5 5) w)) <...> ((_ extract 10 10) w)) #b0))
   (check-sat)
   (get-value)
   ...
   (reset)


Coding Conventions
==================


Compiler Version and C++14
--------------------------

Verilator requires C14. Verilator does not require any newer versions, but
is maintained to build successfully with C17/C20.


Indentation and Naming Style
----------------------------

We will work with contributors to fix up indentation style issues, but it
is appreciated if you could match our style for C/C++:

- Use "mixedCapsSymbols" instead of "underlined_symbols".

- Use a "p" suffix on variables that are pointers, e.g., "nodep".

- Comment every member variable.

- In the include directory, use /// to document functions the user calls.
  (This convention has not been applied retroactively.)

C and Python indentation is automatically maintained with "make format"
using clang-format version 18, and yapf for Python, and is automatically
corrected in the CI actions. For those manually formatting C code:

- Use four spaces per level, and no tabs.

- Use two spaces between the end of source and the beginning of a comment.

- Use one space after if/for/switch/while and similar keywords.

- No spaces before semicolons, nor between a function's name and open
  parenthesis (only applies to functions; if/else has a following space).

Verilog indentation is not yet automatically maintained, but:

- Use two spaces per level, and no tabs.

- Place `begin` on the same line as the earlier starting statement e.g.
  `if`, and place the `end` on a separate line indented the same as the
  starting statement.


Commit messages
---------------

Pull requests do not typically edit ``Changes`` in order to reduce
potential merge conflicts. Instead, contributors should use an appropriate
first line of the git commit message. Maintainers periodically run
``nodist/log_changes`` which analyzes the commit messages to suggest edits
to the ``Changes`` file.

The format of a commit message should be typically of the forms below. The
github issue number, and github pull number if different should be
included.

"Add <some feature> (#1234)"
   For adding a new feature.

"Fix <some item> (#1234)"
   For a bug fix.

"Improve <some item> (#1234)"
   For improving something that previously existed.

"Optimize <some item> (#1234)"
   For improving performance.

"Support <some feature> (#1234)"
   For features that are described by IEEE were previously not supported.

"Commentary"
   For a super-trivial change to e.g. documentation. These will typically
   not get described in the Changes file.

"CI: <Add/Improve/etc as above> (#1234)"
   For changes that only concern github actions. These will typically not
   get described in the Changes file.

"Tests: <Add/Improve/etc as above> (#1234)"
   For changes that only concern tests. These will typically not get
   described in the Changes file.

"Internals: <Add/Improve/etc as above> (#1234)"
   For changes that only concern developers, e.g. code cleanups. These will
   typically not get described in the Changes file.

The line's grammar should match the phrasing used in the Changes file.

If the change does not affect user-visible function, also add "No
functional change." if it's 99% certain not to change behavior, or "No
functional change expected." if no change was expected but there may be
uncertainty.


The ``astgen`` Script
---------------------

The ``astgen`` script is used to generate some of the repetitive C++ code
related to the ``AstNode`` type hierarchy. An example is the abstract
``visit`` methods in ``VNVisitor``. There are other uses; please see the
``*__gen*`` files in the build directories and the ``astgen`` script for
details. A description of the more advanced features of ``astgen`` are
provided here.


Generating ``AstNode`` members
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Some of the member s of ``AstNode`` sub-classes are generated by
``astgen``. These are emitted as pre-processor macro definitions, which
then need to be added to the ``AstNode`` sub-classes they correspond to.
Specifically ``class AstFoo`` should contain an instance of
``ASTGEN_MEMBERS_AstFoo;`` at class scope. The ``astgen`` script checks and
errors if this is not present. The method generated depends on whether the
class is a concrete final class, or an abstract ``AstNode*`` base-class,
and on ``@astgen`` directives present in comment sections in the body of
the ``AstNode`` sub-class definitions.


List of ``@astgen`` directives
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

``@astgen`` directives in comments contained in the body of ``AstNode``
sub-class definitions are parsed and contribute to the code generated by
``astgen``. The general syntax is ``@astgen <keywords> := <description>``,
where ``<keywords>`` determines what is being defined, and
``<description>`` is a ``<keywords>`` dependent description of the
definition. The list of ``@astgen`` directives are as follows:


``op<N>`` operand directives
++++++++++++++++++++++++++++

The ``op1``, ``op2``, ``op3`` and ``op4`` directives are used to describe
the name and type of the up to 4 child operands of a node. The syntax of
the ``<description>`` field is ``<identifier> : <type>``, where
``<identifier>`` will be used as the base name of the generated operand
accessors, and ``<type>`` is one of:

1. An ``AstNode`` sub-class, defining the operand to be of that type,
   always no-null, and with an always null ``nextp()``. That is, the child
   node is always present, and is a single ``AstNode`` (as opposed to a
   list).

2. ``Optional[<AstNode sub-class>]``. This is just like in point 1 above,
   but defines the child node to be optional, meaning it may be null.

3. ``List[AstNode sub-class]`` describes a list operand, which means the
   child node may have a non-null ``nextp()`` and in addition the child
   itself may be null, representing an empty list.

An example of the full syntax of the directive is ``@astgen op1 := lhsp :
AstNodeExpr``.

``astnode`` generates accessors for the child nodes based on these
directives. For non-list children, the names of the getter and setter both
are that of the given ``<identifier>``. For list-type children, the getter
is ``<identifier>``, and instead of the setter, there an
``add<Identifier>`` method is generated that appends new nodes (or lists of
nodes) to the child list.


``alias op<N>`` operand alias directives
++++++++++++++++++++++++++++++++++++++++

If a super-class already defined a name and type for a child node using the
``op<N>`` directive, but a more appropriate name exists in the context of a
sub-class, then the alias directive can be used to introduce an additional
name for the child node. The is ``alias op<N> := <identifier>`` where
``<identifier>`` is the new name. ``op<N>`` must have been defined in some
super-class of the current node.

Example: ``@astgen alias op1 := condp``


Generating ``DfgVertex`` sub-classes
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Most of the ``DfgVertex`` sub-classes are generated by ``astgen``, from the
definitions of the corresponding ``AstNode`` vertices.


Additional features of ``astgen``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

In addition to generating ``AstNode`` members as described above,
``astgen`` is also use to handle some of the repetitive implementation code
that is still variable enough not to be handled in C++ macros.

In particular, ``astgen`` is used to pre-process some of the C++ source
files. For example in ``V3Const.cpp``, it is used to implement the
``visit()`` functions for each binary operation using the ``TREEOP`` macro.

The original C++ source code is transformed into C++ code in the
``obj_opt`` and ``obj_dbg`` sub-directories (the former for the optimized
version of Verilator, the latter for the debug version). So for example
``V3Const.cpp`` into ``V3Const__gen.cpp``.


Visitor Functions
-----------------

Verilator uses the "Visitor" design pattern to implement its refinement and
optimization passes. This allows separation of the pass algorithm from the
AST on which it operates. Wikipedia provides an introduction to the concept
at https://en.wikipedia.org/wiki/Visitor_pattern.

As noted above, all visitors are derived classes of ``VNVisitor``. All
derived classes of ``AstNode`` implement the ``accept`` method, which takes
as argument a reference to an instance or a ``VNVisitor`` derived class and
applies the visit method of the ``VNVisitor`` to the invoking AstNode
instance (i.e. ``this``).

One possible difficulty is that a call to ``accept`` may perform an edit
which destroys the node it receives as an argument. The
``acceptSubtreeReturnEdits`` method of ``AstNode`` is provided to apply
``accept`` and return the resulting node, even if the original node is
destroyed (if it is not destroyed, it will just return the original node).

The behavior of the visitor classes is achieved by overloading the
``visit`` function for the different ``AstNode`` derived classes. If a
specific implementation is not found, the system will look in turn for
overloaded implementations up the inheritance hierarchy. For example
calling ``accept`` on ``AstIf`` will look in turn for:

::

   void visit(AstIf* nodep)
   void visit(AstNodeIf* nodep)
   void visit(AstNodeStmt* nodep)
   void visit(AstNode* nodep)

There are three ways data is passed between visitor functions.

1. A visitor-class member variable. This is generally for passing "parent"
   information down to children. ``m_modp`` is a common example. It's set
   to NULL in the constructor, where that node (``AstModule`` visitor) sets
   it, then the children are iterated, then it's cleared. Children under an
   ``AstModule`` will see it set, while nodes elsewhere will see it clear.
   If there can be nested items (for example an ``AstFor`` under an
   ``AstFor``) the variable needs to be save-set-restored in the ``AstFor``
   visitor; otherwise exiting the lower for will lose the upper for's
   setting.

2. User attributes. Each ``AstNode`` (**Note.** The AST node, not the
   visitor) has five user attributes, which may be accessed as an integer
   using the ``user1()`` through ``user4()`` methods, or as a pointer (of
   type ``AstNUser``) using the ``user1p()`` through ``user4p()`` methods
   (a common technique lifted from graph traversal packages).

   A visitor first clears the one it wants to use by calling
   ``AstNode::user#ClearTree()``, then it can mark any node's ``user#()``
   with whatever data it wants. Readers just call ``nodep->user()``, but
   may need to cast appropriately, so you'll often see
   ``VN_CAST(nodep->userp(), SOMETYPE)``. At the top of each visitor are
   comments describing how the ``user()`` stuff applies to that visitor
   class. For example:

   ::

      // NODE STATE
      // Cleared entire netlist
      //   AstModule::user1p()     // bool. True to inline this module

   This says that at the ``AstNetlist`` ``user1ClearTree()`` is called.
   Each AstModule's `user1()` is used to indicate if we're going to inline
   it.

   These comments are important to make sure a ``user#()`` on a given
   ``AstNode`` type is never being used for two different purposes.

   Note that calling ``user#ClearTree`` is fast; it doesn't walk the tree,
   so it's ok to call fairly often. For example, it's commonly called on
   every module.

3. Parameters can be passed between the visitors in close to the "normal"
   function caller to callee way. This is the second ``vup`` parameter of
   type ``AstNUser`` that is ignored on most of the visitor functions.
   V3Width does this, but it proved messier than the above and is
   deprecated. (V3Width was nearly the first module written. Someday this
   scheme may be removed, as it slows the program down to have to pass vup
   everywhere.)


Iterators
---------

``VNVisitor`` provides a set of iterators to facilitate walking over the
tree. Each operates on the current ``VNVisitor`` class (as this) and takes
an argument type ``AstNode*``.

``iterate``
   Applies the ``accept`` method of the ``AstNode`` to the visitor
   function.

``iterateAndNextIgnoreEdit``
   Applies the ``accept`` method of each ``AstNode`` in a list (i.e.
   connected by ``nextp`` and ``backp`` pointers).

``iterateAndNextNull``
   Applies the ``accept`` method of each ``AstNode`` in a list, only if the
   provided node is non-NULL. If a node is edited by the call to
   ``accept``, apply ``accept`` again, until the node does not change.

``iterateListBackwards``
   Applies the ``accept`` method of each ``AstNode`` in a list, starting
   with the last one.

``iterateChildren``
   Applies the ``iterateAndNextNull`` method on each child ``op1p`` through
   ``op4p`` in turn.

``iterateChildrenBackwards``
   Applies the ``iterateListBackwards`` method on each child ``op1p``
   through ``op4p`` in turn.


Caution on Using Iterators When Child Changes
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Visitors often replace one node with another node; V3Width and V3Const are
major examples. A visitor which is the parent of such a replacement needs
to be aware that calling iteration may cause the children to change. For
example:

::

   // nodep->lhsp() is 0x1234000
   iterateAndNextNull(nodep->lhsp());  // and under covers nodep->lhsp() changes
   // nodep->lhsp() is 0x5678400
   iterateAndNextNull(nodep->lhsp());

Will work fine, as even if the first iterate causes a new node to take the
place of the ``lhsp()``, that edit will update ``nodep->lhsp()``, and the
second call will correctly see the change. Alternatively:

::

   lp = nodep->lhsp();
   // nodep->lhsp() is 0x1234000, lp is 0x1234000
   iterateAndNextNull(lp); **lhsp=NULL;**  // and under covers nodep->lhsp() changes
   // nodep->lhsp() is 0x5678400, lp is 0x1234000
   iterateAndNextNull(lp);

This will cause bugs or a core dump, as lp is a dangling pointer. Thus it
is advisable to set lhsp=NULL shown in the \*'s above to make sure these
dangles are avoided. Another alternative used in special cases, mostly in
V3Width, is to use acceptSubtreeReturnEdits, which operates on a single
node and returns the new pointer if any. Note acceptSubtreeReturnEdits does
not follow ``nextp()`` links.

::

   lp = acceptSubtreeReturnEdits(lp)


Identifying Derived Classes
---------------------------

A common requirement is to identify the specific ``AstNode`` class we are
dealing with. For example, a visitor might not implement separate ``visit``
methods for ``AstIf`` and ``AstGenIf``, but just a single method for the
base class:

::

   void visit(AstNodeIf* nodep)

However that method might want to specify additional code if it is called
for ``AstGenIf``. Verilator does this by providing a ``VN_IS`` method for
each possible node type, which returns true if the node is of that type (or
derived from that type). So our ``visit`` method could use:

::

   if (VN_IS(nodep, AstGenIf) {
     <code specific to AstGenIf>
   }

Additionally the ``VN_CAST`` method converts pointers similar to C++
``dynamic_cast``. This either returns a pointer to the object cast to that
type (if it is of class ``SOMETYPE``, or a derived class of ``SOMETYPE``)
or else NULL. (However, for true/false tests, use ``VN_IS`` as that is
faster.)


.. _testing:

Testing
=======

For an overview of how to write a test, see the BUGS section of the
`Verilator Manual <https://verilator.org/verilator_doc.html>`_.

It is important to add tests for failures as well as success (for example
to check that an error message is correctly triggered).

Tests that fail should, by convention have the suffix ``_bad`` in their
name, and include ``fails = 1`` in either their ``compile`` or ``execute``
step as appropriate.


Preparing to Run Tests
----------------------

For all tests to pass, you must install the following packages:

- SystemC to compile the SystemC outputs, see https://systemc.org

- wavediff to find differences in waveform outputs. See the readme at
  https://github.com/hudson-trading/wavetools

- Cmake for build paths that use it.


Controlling the Test Driver
---------------------------

The test driver script `driver.py` runs tests; see the `Test Driver`
section. The individual test drivers are written in Perl; see `Test
Language`.


Manual Test Execution
---------------------

A specific regression test can be executed manually. To start the "EXAMPLE"
test, run the following command.

::

   test_regress/t/t_EXAMPLE.py


Regression Testing for Developers
---------------------------------

Developers will also want to call ./configure with two extra flags:

``--enable-ccwarn``
   This causes the build to stop on warnings as well as errors. A good way
   to ensure no sloppy code gets added; however it can be painful when it
   comes to testing, since third party code used in the tests (e.g.
   SystemC) may not be warning free.

``--enable-longtests``
   In addition to the standard C, SystemC examples, also run the tests in
   the ``test_regress`` directory when using *make test*'. This is disabled
   by default, as SystemC installation problems would otherwise falsely
   indicate a Verilator problem.

There are some traps to avoid when running regression tests

- When checking the MANIFEST, the test will fail on unexpected code in the
  Verilator tree. So make sure to keep any such code outside the tree.

- Not all Linux systems install Perldoc by default. This is needed for the
  ``--help`` option to Verilator, and also for regression testing. This can
  be installed using CPAN:

  ::

     cpan install Pod::Perldoc

  Many Linux systems also offer a standard package for this. Red
  Hat/Fedora/Centos offer *perl-Pod-Perldoc*', while Debian/Ubuntu/Linux
  Mint offer \`perl-doc'.

- Running regression may exhaust resources on some Linux systems,
  particularly file handles and user processes. Increase these to
  respectively 16,384 and 4,096. The method of doing this is
  system-dependent, but on Fedora Linux it would require editing the
  ``/etc/security/limits.conf`` file as root.

Diffing generated code after changes
------------------------------------

When making a change in the code generation area that should not change the
actual emitted code, it is useful to perform a diff to make sure the
emitted code really did not change. To do this, the top level Makefile
provides the *test-snap* and *test-diff* targets:

- Run the test suite with ``make test``
- Take a snapshot with ``make test-snap``
- Apply your changes
- Run the test suite again with ``make test``
- See the changes in the output with ``make test-diff``

Continuous Integration
----------------------

Verilator uses GitHub Actions which automatically tests the master branch
for test failures on new commits. It also runs a daily cron job to validate
all tests against different OS and compiler versions.

Developers can enable Actions on their GitHub repository so that the CI
environment can check their branches too by enabling the build workflow:

- On GitHub, navigate to the main page of the repository.

- Under your repository name, click Actions.

- In the left sidebar, click the workflow you want to enable ("build").

- Click Enable workflow.

Benchmarking
------------

For benchmarking the effects of changes (simulation speed, memory
consumption, Verilation time, etc.), you can use `RTLMeter
<https://github.com/verilator/rtlmeter>`__, a benchmark suite designed for
this purpose. The scripts provided with RTLMeter have many capabilities.
For full details, see the `documentation of RTLMeter
<https://verilator.github.io/rtlmeter>`__ itself.

For a quick check, you an run the following after putting ``verilator`` on
your ``PATH``:

.. code-block:: shell

   ./rtlmeter run --cases "+standard" --workRoot work-a
   ./rtlmeter report work-a

To compare against an alternate version, again put that alternate
``verilator`` on your ``PATH`` then run:

.. code-block:: shell

   ./rtlmeter run --cases "+standard" --workRoot work-b
   ./rtlmeter compare work-a work-b

The continuous integration system in GitHub Actions runs this benchmark
suite nightly on the master branch. The performance numbers from these
nightly runs can be viewed via the `RTLMeter results dashboard
<https://verilator.github.io/verilator-rtlmeter-results>`__. Note that
these results are collected on GitHub hosted runners. These are virtual
machines operating in a potentially noisy environment, so time measurements
can have significant variance. Experience shows that a ~20% time difference
can be reliably measured on GitHub hosted runners, and smaller differences
are noticeable over a few days of reruns as trends emerge from the noise.

Code Coverage
-------------

Code coverage for developing Verilator itself can be collected using
``gcc`` and ``gcov`` with the following flow. Note that configuring with
``--enable-dev-gcov`` disables optimization for both the debug and
optimized builds of Verilator, so running the resulting executables can be
slow:

.. code-block:: shell

   ./configure --enable-longtests --enable-dev-gcov
   make -j$(nproc)        # Build verilator
   make test              # Run the tests - this will generate .gcda files
   make coverage-view     # Create and open HTML coverate reprot


The ``coverage-view`` make target opens the generated coverage report. This
depends on ``coverage-report``, which generates the HTML coverage report.
That turn depends on ``coverage-combine``, which combines all ``.gcda``
files into an lcov data file. Each of these make targets can be used
separately if desired.

You can also use the ``coverage-zero`` target to remove all ``.gcda``
files, which in effect clears all collected coverage data. This can be
useful when checking individual test cases while modifying them.

To collect coverage only for select tests, instead of ``make test``, run
the individual ``test_regress/t/*.py`` cases, then use ``make
coverage-view`` as before.

Every time binary built with coverage collection is invoked, it will add
the coverage data collected during that invocation to the existing
``.gcda`` files. This means that ``verilator`` can also be run on any other
external files or projects to collect code coverage on those invocations.

Be aware if changing a test, if that test no longer covers some item, the
report will still contain the old coverage. Use ``make coverage-zero`` and
rerun all tests if this is a concern.

It is also possible to generate a 'patch coverage' report, which will only
contain information about lines modified compared to a Git ref specified by
the ``COVERAGE_BASE`` Make variable (including uncommitted changes). For
example, to see coverage of changes compared to upstream, use:

.. code-block:: shell

   make coverage-view COVERAGE_BASE=origin/master


Code Coverage Results
---------------------

When code coverage is complete, line coverage should be at 100%. Branch
coverage is not required to be at 100%, but all uncovered branches should
be understood as to why.

For lines which are impossible, use ``// LCOV_EXCL_LINE``, or ``//
LCOV_EXCL_START`` and ``// LCOV_EXCL_STOP`` regions.

For branches which are impossible, use the ``VL_UNCOVERABLE(condition)``
macro around uncoverable conditions, otherwise use ``//
LCOV_EXCL_BR_LINE``.

The assertions ``UASSERT`` and similar are automatically excluded from
coverage, and as such should not require exclusion meta comments.

As getting to complete code coverage typically takes many passes,
developers should please iterate coverage improvements using a personal
machine, or a personal branch on GitHub with personal action runners,
rather than the Verilator GitHub runners, to avoid lots of mail to watchers
and CI queue blockage.


Fuzzing
-------

There are scripts included to facilitate fuzzing of Verilator. These have
been successfully used to find a number of bugs in the frontend.

The scripts are based on using `American fuzzy lop
<https://lcamtuf.coredump.cx/afl/>`__ on a Debian-like system.

To get started, cd to "nodist/fuzzer/" and run "./all". A sudo password may
be required to setup the system for fuzzing.


Debugging
=========


Debug Levels
------------

The "UINFO" calls in the source indicate a debug level. Messages level 3
and below are globally enabled with ``--debug``. Higher levels may be
controlled with ``--debugi <level>``. An individual source file levels may
be controlled with ``-debugi-<srcfile> <level>``. For example ``--debug
--debugi 5 --debugi-V3Width 9`` will use the debug binary at default debug
level 5, with the V3Width.cpp file at level 9.


`--debug`
---------

When you run with ``--debug``, there are three primary output file types
placed into the obj_dir, .vpp, .tree and .dot files.

.vpp Output
-----------

Verilator creates a *{mod_prefix}*\ __inputs.vpp file containing all the
files that were read, filtered by preprocessing. This file can be fed back
into Verilator, replacing on the command line all of the previous input
files, to enable simplification of test cases. This file also contains most
command line arguments Verilator was invoked as `// verilator fargs``
metacomments, with and can be parsed by `\`-f`. So to reproduce the run
that created the file, run:

::
   verilator -f <prefix>__inputs.vpp <prefix>__inputs.vpp

The first `-f <prefix>__inputs.vpp` reads the command line arguments from
the file. The second `<prefix>__inputsl.vpp` adds the file as a normal
source file.

Verilator also creates .vpp files for each individual file passed on the
command line. These do not contain the command line argument metacomments.


.dot Output
-----------

Dot files are dumps of internal graphs in `GraphViz
<https://www.graphviz.org>`__ dot format. When a dot file is dumped,
Verilator will also print a line on stdout that can be used to format the
output, for example:

::

   dot -Tps -o ~/a.ps obj_dir/Vtop_foo.dot

You can then print a.ps. You may prefer gif format, which doesn't get
scaled so it can be more useful with large graphs.

For interactive graph viewing consider `xdot
<https://github.com/jrfonseca/xdot.py>`__ or `ZGRViewer
<https://zvtm.sourceforge.net/zgrviewer.html>`__. If you know of better
viewers (especially for large graphs) please let us know.


.tree Output
------------

Tree files are dumps of the AST Tree and are produced between every major
algorithmic stage. An example:

::

     NETLIST 0x90fb00 <e1> {a0ah}
    1: MODULE 0x912b20 <e8822> {a8ah}  top  L2 [P]
   *1:2: VAR 0x91a780 <e74#> {a22ah} @dt=0xa2e640(w32)  out_wide [O] WIRE
    1:2:1: BASICDTYPE 0xa2e640 <e2149> {e24ah} @dt=this(sw32)  integer kwd=integer range=[31:0]

The following summarizes the above example dump, with more detail on each
field in the section below.

============= =============================================================
``1:2:``      The hierarchy of the ``VAR`` is the ``op2p`` pointer under
              the ``MODULE``, which in turn is the ``op1p`` pointer under
              the ``NETLIST``.
``VAR``       The AstNodeType (e.g. ``AstVar``).
``0x91a780``  Address of this node.
``<e74>``     The 74th edit to the netlist was the last modification to
              this node.
``{a22ah}``   This node is related to the source filename "a", where "a" is
              the first file read, "z" the 26th, and "aa" the 27th. Then
              line 22 in that file, then column 8 (aa=0, az=25, ba=26,
              ...).
``@dt=0x...`` The address of the data type this node references.
``w32``       The data-type width() is 32 bits.
``out_wide``  The name() of the node, in this case, the name of the
              variable.
``[O]``       Flags which vary with the type of node, in this case of a
              VAR, it means the variable is an output.
============= =============================================================

In more detail, the following fields are dumped common to all nodes. They
are produced by the ``AstNode::dump()`` method:

Tree Hierarchy
   The dump lines begin with numbers and colons to indicate the child node
   hierarchy. As noted above, ``AstNode`` has lists of items at the same
   level in the AST, connected by the ``nextp()`` and ``prevp()`` pointers.
   These appear as nodes at the same level. For example, after inlining:

   ::

       NETLIST 0x929c1c8 <e1> {a0} w0
      1: MODULE 0x92bac80 <e3144> {e14} w0  TOP_t  L1 [P]
      1:1: CELLINLINE 0x92bab18 <e3686#> {e14} w0  v -> t
      1:1: CELLINLINE 0x92bc1d8 <e3688#> {e24} w0  v__DOT__i_test_gen -> test_gen
      ...
      1: MODULE 0x92b9bb0 <e503> {e47} w0  test_gen  L3
      ...

AstNode type
   The textual name of this node AST type (always in capitals). Many of
   these correspond directly to Verilog entities (for example ``MODULE``
   and ``TASK``), but others are internal to Verilator (for example
   ``NETLIST`` and ``BASICDTYPE``).

Address of the node
   A hexadecimal address of the node in memory. Useful for examining with
   the debugger. If the actual address values are not important, then using
   the ``--dump-tree-addrids`` option will convert address values to short
   identifiers of the form ``([A-Z]*)``, which is hopefully easier for the
   reader to cross-reference throughout the dump.

Last edit number
   Of the form ``<ennnn>`` or ``<ennnn#>`` , where ``nnnn`` is the number
   of the last edit to modify this node. The trailing ``#`` indicates the
   node has been edited since the last tree dump (typically in the last
   refinement or optimization pass). GDB can watch for this; see
   `Debugging`.

Source file and line
   Of the form ``{xxnnnn}``, where C{xx} is the filename letter (or
   letters) and ``nnnn`` is the line number within that file. The first
   file is ``a``, the 26th is ``z``, the 27th is ``aa``, and so on.

User pointers
   Shows the value of the node's user1p...user4p, if non-NULL.

Data type
   Many nodes have an explicit data type. "@dt=0x..." indicates the address
   of the data type (AstNodeDType) this node uses.

   If a data type is present and is numeric, it then prints the width of
   the item. This field is a sequence of flag characters and width data as
   follows:

   - ``s`` if the node is signed.

   - ``d`` if the node is a double (i.e. a floating point entity).

   - ``w`` always present, indicating this is the width field.

   - ``u`` if the node is unsized.

   - ``/nnnn`` if the node is unsized, where ``nnnn`` is the minimum
        width.

Name of the entity represented by the node if it exists
   For example, for a ``VAR`` is the name of the variable.

Many nodes follow these fields with additional node-specific information.
Thus the ``VARREF`` node will print either ``[LV]`` or ``[RV]`` to indicate
a left value or right value, followed by the node of the variable being
referred to. For example:

::

   1:2:1:1: VARREF 0x92c2598 <e509> {e24} w0  clk [RV] <- VAR 0x92a2e90 <e79> {e18} w0  clk [I] INPUT

In general, examine the ``dump()`` method in ``V3AstNodes.cpp`` of the node
type in question to determine additional fields that may be printed.

The ``MODULE`` has a list of ``CELLINLINE`` nodes referred to by its
``op1p()`` pointer, connected by ``nextp()`` and ``prevp()`` pointers.

Similarly, the ``NETLIST`` has a list of modules referred to by its
``op1p()`` pointer.


.tree.json Output
-----------------

``.tree.json``` is an alternative dump format to ``.tree`` that is meant
for programmatic processing (e.g. with `astsee
<https://github.com/antmicro/astsee>`_). To enable this dump format, use
:vlopt:`--dump-tree-json` or :vlopt:`--json-only`.

The potential fields in the JSON dump can be determined by searching for
JSON in the `src/V3AstNodes.cpp` source file. The dump will only include
booleans that are true, omitting those that are false.

Structure example:

::

   {
     /* Attributes that are common to most types of nodes */
     "type": "VAR",
     "name": "cyc",
     "verilogName": "cyc",
     /* By default addresses and filenames use short/stable ids rather than real value */
     "addr": "(H)",
     "loc": "a,25:12,26:15", /* "fileid,firstLine:firstCol,lastLine:endCol" (endCol is right exclusive) */
     "editNum": 602,
     /* Some fields that are specific to AstVar nodes:  */
     "origName": "cyc",
     "ioDirection": "NONE",
     "attrClocker": "UNKNOWN",
     "lifetime": "NONE",
     "varType": "VAR",
     /* Lists of child nodes (which use similar structure as their parent): */
     "childDTypep": [ /* ... */ ],
     "delayp": [ /* ... */ ],
     "valuep": [ /* ... */ ],
     "attrsp": [ /* ... */ ]
   }

.tree.meta.json Output
----------------------

.tree.meta.json contains metadata that is common across the whole AST tree
(in case of --dump-tree-json, multiple trees share one meta file).

Besides de-duplication of data shared between multiple stages, .meta.json
enables offloading unstable data (that can vary from machine-to-machine or
run-to-run) from main .tree.json. This offloading allows, for example, to
use byte-to-byte comparisons of AST dumps in tests.

::

   {"files": {
     /* Map id to filename, and other metadata */
     "d": {"filename":"/home/ant/tmp/verilator/include/verilated_std.sv", "realpath":"/home/ant/tmp/verilator/include/verilated_std.sv", "language":"1800-2023"},
     "a": {"filename":"<built-in>", "realpath":"<built-in>", "language":"1800-2023"},
     "b": {"filename":"<command-line>", "realpath":"<command-line>", "language":"1800-2023"},
     "c": {"filename":"input.vc", "realpath":"/home/ant/tmp/verilator/test_regress/input.vc", "language":"1800-2023"},
     "e": {"filename":"t/t_EXAMPLE.v", "realpath":"/home/ant/tmp/verilator/test_regress/t/t_EXAMPLE.v", "language":"1800-2023"}
    },"pointers": {
     /* Map id to real address */
     "(AG)": "0x562997289180",
     "(YF)": "0x5629971c50b0",
     "(WF)": "0x5629971e7ae0",
     /* ... /*
    },"ptrFieldNames": [
     /* List of fields that are used for storing pointers */
     "aboveScopep",
     "voidp",
     "addr",
     /* ... */
   ]}


.tree.dot Output
----------------

``*.tree.dot`` files are dumps of the AST Tree in `GraphViz
<https://www.graphviz.org>`__ dot format. This can be used to visualize the
AST Tree. The vertices correspond to ``AstNode`` instances, and the edges
represent the pointers (``op1p``, ``op2p``, etc) between the nodes.


Debugging with GDB
------------------

The `driver.py` script accepts ``--debug --gdb`` to start Verilator under
gdb and break when an error is hit, or the program is about to exit. You
can also use ``--debug --gdbbt`` to just backtrace and then exit gdb. To
debug the Verilated executable, use ``--gdbsim``.

If you wish to start Verilator under GDB (or another debugger), then you
can use ``--debug`` and look at the underlying invocation of
``verilator_dbg``. For example

::

   t/t_alw_dly.py --debug

shows it invokes the command:

::

   ../verilator_bin_dbg --prefix Vt_alw_dly --x-assign unique --debug
     -cc -Mdir obj_dir/t_alw_dly --debug-check -f input.vc t/t_alw_dly.v

Start GDB, then ``start`` with the remaining arguments.

::

   gdb ../verilator_bin_dbg
   ...
   (gdb) start --prefix Vt_alw_dly --x-assign unique --debug -cc -Mdir
             obj_dir/t_alw_dly --debug-check  -f input.vc t/t_alw_dly.v
             > obj_dir/t_alw_dly/vlt_compile.log
   ...
   Temporary breakpoint 1, main (argc=13, argv=0xbfffefa4, env=0xbfffefdc)
       at ../Verilator.cpp:615
   615         ios::sync_with_stdio();
   (gdb)

You can then continue execution with breakpoints as required.

To break at a specific edit number which changed a node (presumably to find
what made a <e#*#*> line in the tree dumps):

::

   watch AstNode::s_editCntGbl=####

Then, when the watch fires, to break at every following change to that
node:

::

   watch m_editCount

To print a node:

::

   pn nodep
   # or: call dumpGdb(nodep)  # aliased to "pn" in src/.gdbinit
   pnt nodep
   # or: call dumpTreeGdb(nodep)  # aliased to "pnt" in src/.gdbinit

``src/.gdbinit`` and ``src/.gdbinit.py`` define handy utilities for working
with JSON AST dumps. For example:

* ``jstash nodep`` - Perform a JSON AST dump and save it into GDB value
  history (e.g. ``$1``)

* ``jtree nodep`` - Perform a JSON AST dump and pretty print it using
  ``astsee_verilator``.
* ``jtree $1`` - Pretty print a dump that was previously saved by
  ``jstash``.
* ``jtree nodep -d '.file, .timeunit'`` - Perform a JSON AST dump, filter
  out some fields and pretty print it.

* ``jtree 0x55555613dca0`` - Pretty print using address literal (rather
  than actual pointer).
* ``jtree $1 nodep`` - Diff ``nodep`` against an older dump.

A detailed description of ``jstash`` and ``jtree`` can be displayed using
``gdb``'s ``help`` command.

These commands require `astsee <https://github.com/antmicro/astsee>`_ to be
installed.

When GDB halts, it is useful to understand that the backtrace will commonly
show the iterator functions between each invocation of ``visit`` in the
backtrace. You will typically see a frame sequence something like:

::

   ...
   visit()
   iterateChildren()
   iterateAndNext()
   accept()
   visit()
   ...

Bisecting bad transformations
-----------------------------

If a bad transformation in the internals of Verilator causes a failure only
at runtime, it can be found fairly automatically by only applying the
transform a limited number of times, then performing a bisection search
over the limit to pinpoint the exact transformation that introduces the
failure.

To facilitate this an instance of the ``V3DebugBisect`` class can be used
in conjunction with the ``verilator_bisect`` script.

In the offending algorithm, create a static instance of ``V3DebugBisect``:

::

   static V3DebugBisect s_debugBisect{"TransformName"};

Call the ``stop`` method before applying a transformation, and do not
proceed if it returns ``false``. Then use ``verilator_bisect`` to search an
interval of values. You need to provide an arbitrary discriminator command,
this should run Verilator, then any necessary checks (e.g.: simulation) to
detect that the failure is still present. It should exit with a non-zero
status if the failure is still present. The discriminator command can
otherwise be arbitrarily complex, the actual search limit is passed via
environment variables. E.g.:

::

   bin/verilator_bisect DfgPeephole 0 1000 test_regress/t/t_myothertest.py

An additional command can be run before the discriminator command. E.g.
this will run RTLMeter, but first removes its working directory so the
models are recompiled on every step:

::

   bin/verilator_bisect --pre "rm -rf work-bisect" DfgPeephole 0 10000000 \
     rtlmeter run --cases "..." --workRoot=work-bisect

When the bisection ends, the first value that makes the discriminator
command fail is printed, which identifies the exact offending application
of the transform.

Debugging Non-Deterministic Results
-----------------------------------

The Verilator binary should be deterministic, producing the same Verilated
C++ and stdout when run multiple times with the same inputs. A common
source of non-determinism is (mis-)use of pointers in container
comparisons, causing arbitrary changes im memory layout to make container
comparisons differ, and in turn creating differences in Ast layout, and
ultimately different C++ code.

To debug this, compile Verilator with the `VL_ALLOC_RANDOM_CHECKS` define,
then Verilator may be run with a seed e.g. `-debug-alloc-random 22` and
`-debug-alloc-random 33` which will randomly change the memory allocation
order. If multiple seeds are run, `verilator_difftree` may then be used to
see where netlists start to differ. Once the Ast difference is determined,
the debugging tips above can then further isolate the C++ code causing a
specific node to be created or changed.

The typical fix is to add a comparison operator to the problematic
container, e.g. compare using `AstNodeComparator` or by `name()`.

The following script is an example of this debugging:

.. code-block:: bash

   # Before this: ./configure CXX="g++ -DVL_ALLOC_RANDOM_CHECKS" && make -j
   # Example script for finding nondeterminism
   cd $VERILATOR_ROOT
   set -e
   t=t_split_var_0  # Test name
   test_regress/t/$t.py --debug --debug-alloc-random 33 --dumpi-tree 9 --obj-suffix .33
   test_regress/t/$t.py --debug --debug-alloc-random 66 --dumpi-tree 9 --obj-suffix .66
   bin/verilator_difftree test_regress/obj_vlt/$t{.33,.66} | grep -v CFILE | grep -v "^ " | grep -v /tmp | grep -v @@


Adding a New Feature
====================

Generally, what would you do to add a new feature?

1. File an issue (if there isn't already) so others know what you're
   working on.

2. Make a testcase in the test_regress/t/t_EXAMPLE format, see `Testing`.

3. If grammar changes are needed, look at the IEEE 1800-2023 Appendix A, as
   src/verilog.y generally follows the same rule layout.

4. If a new Ast type is needed, add it to the appropriate V3AstNode*.h.
   Follow the convention described above about the AstNode type hierarchy.
   Ordering of definitions is enforced by ``astgen``.

5. Now you can run ``test_regress/t/t_<newtestcase>.py --debug`` and it'll
   probably fail, but you'll see a
   ``test_regress/obj_dir/t_<newtestcase>/*.tree`` file which you can
   examine to see if the parsing worked. See also the sections above on
   debugging.

6. Modify the later visitor functions to process the new feature as needed.


Adding a New Pass
-----------------

For more substantial changes, you may need to add a new pass. The simplest
way to do this is to copy the ``.cpp`` and ``.h`` files from an existing
pass. You'll need to add a call into your pass from the ``process()``
function in ``src/verilator.cpp``.

To get your pass to build, you'll need to add its binary filename to the
list in ``src/Makefile_obj.in`` and reconfigure.


"Never" features
----------------

Verilator ideally would support all of IEEE, and has the goal to get close
to full support. However the following IEEE sections and features are not
anticipated to be ever implemented for the reasons indicated.

IEEE 1800-2023 3.3 modules within modules
   Little/no tool support, and arguably not a good practice.

IEEE 1800-2023 6.12 "shortreal"
   Little/no tool support, and easily promoted to real.

IEEE 1800-2023 11.11 Min, typ, max
   No SDF support, so will always use typical.

IEEE 1800-2023 20.16 Stochastic analysis
   Little industry use.

IEEE 1800-2023 20.17 PLA modeling
   Little industry use and outdated technology.

IEEE 1800-2023 31 Timing checks
   No longer relevant with static timing analysis tools.

IEEE 1800-2023 32 SDF annotation
   No longer relevant with static timing analysis tools.


Test Driver
===========

This section documents the test driver script, `driver.py`. driver.py
invokes Verilator or another simulator on each test file. For test file
contents description see `Test Language`.

The driver reports the number of tests which pass, fail, or skipped (some
resource required by the test is not available, such as SystemC).

There are thousands of tests, and for faster completion you may want to run
the regression tests with OBJCACHE enabled and in parallel on a machine
with many cores. See the `-j` option and OBJCACHE environment variable.


driver.py Pass-Thru Arguments
-----------------------------

Arguments not understood by `driver.py` which begin with `+verilator+` are
passed through as Verilated executable arguments. Other not-understood
arguments are passed as Verilator arguments.


driver.py Non-Scenario Arguments
--------------------------------

.. program:: driver.py

.. option:: --benchmark [<cycles>]

   Show execution times of each step. If an optional number is given,
   specifies the number of simulation cycles (for tests that support it).

.. option:: --debug

   Same as ``verilator --debug``: Use the debug version of Verilator which
   enables additional assertions, debugging messages, and structure dump
   files.

.. option:: --debugi(-<srcfile>) <level>

   Same as ``verilator --debugi level``: Set Verilator internal debugging
   level globally to the specified debug level (1-10).

.. option:: --driver-clean

   After a test passes, remove the generated objects. Reduces storage
   requirements, but may result in longer runtime if the tests are run
   again.

.. option:: --dump-tree

   Same as ``verilator --dump-tree``: Enable Verilator writing .tree debug
   files with dumping level 3, which dumps the standard critical stages.
   For details on the format see `.tree Output`.

.. option:: --fail-max <numtests>

   Set the number of failing tests, after which the driver will stop
   running additional tests. Defaults to 20, 0 disables.

.. option:: --gdb

   Same as ``verilator --gdb``: Run Verilator under the debugger.

.. option:: --gdbbt

   Same as ``verilator --gdbbt``: Run Verilator under the debugger, only to
   print backtrace information. Requires ``--debug``.

.. option:: --gdbsim

   Run Verilator generated executable under the debugger.

.. option:: --golden

   Update golden files, equivalent to ``export HARNESS_UPDATE_GOLDEN=1``.

.. option:: --hashset <set>/<numsets>

   Split tests based on a hash of the test names into <numsets> and run
   only tests in set number <set> (0..<numsets>-1).

.. option:: --help

   Displays help message and exits.

.. option:: --j #

   Run number of parallel tests, or 0 to determine the count based on the
   number of cores installed.

.. option:: --obj-suffix <name>

   Append the argument to the name of the ``test_regress/obj_``
   directories.

.. option:: --quiet

   Suppress all output except for failures and progress messages every 15
   seconds. Intended for use only in automated regressions. See also
   ``--rerun``, and ``--verbose`` which is not the opposite of ``--quiet``.

.. option:: --rerun

   Rerun all tests that failed in this run. Reruns force the flags
   ``--no-quiet --j 1``.

.. option:: --rr

   Same as ``verilator --rr``: Run Verilator and record with ``rr``.

.. option:: --rrsim

   Run Verilator generated executable and record with ``rr``.

.. option:: --site

   Run site-specific tests also.

.. option:: --stop

   Stop on the first error.

.. option:: --top-filename <file>

   Override the default Verilog file name.

.. option:: --trace

   Set the simulator-specific flags to request waveform tracing.

.. option:: --valgrind

   Same as ``verilator --valgrind``: Run Verilator under `Valgrind <https://valgrind.org/>`_.

.. option:: --verbose

   Compile and run the test in verbose mode. This means ``TEST_VERBOSE``
   will be defined for the test (Verilog and any C++/SystemC wrapper).

.. option:: --verilated-debug

   For tests using the standard C++ wrapper, enable runtime debug mode.


driver.py Scenario Arguments
----------------------------

The following options control which simulator is used, and which tests are
run. Multiple flags may be used to run multiple simulators/scenarios
simultaneously.

.. option:: --atsim

   Run ATSIM simulator tests.

.. option:: --dist

   Run simulator-agnostic distribution tests.

.. option:: --ghdl

   Run GHDL simulator tests.

.. option:: --iv

   Run Icarus Verilog simulator tests.

.. option:: --ms

   Run ModelSim simulator tests.

.. option:: --nc

   Run Cadence NC-Verilog simulator tests.

.. option:: --vcs

   Run Synopsys VCS simulator tests.

.. option:: --vlt

   Run Verilator tests in single-threaded mode. Default unless another
   scenario flag is provided.

.. option:: --vltmt

   Run Verilator tests in multithreaded mode.

.. option:: --xrun

   Run Cadence Xcelium simulator tests.

.. option:: --xsim

   Run Xilinx XSim simulator tests.


driver.py Environment
---------------------

HARNESS_UPDATE_GOLDEN
   If true, update all .out golden reference files. Typically, instead the
   ``--golden`` option is used to update only a single test's reference.

SYSTEMC
   Root directory name of SystemC kit. Only used if ``SYSTEMC_INCLUDE`` not
   set.

SYSTEMC_INCLUDE
   Directory name with systemc.h in it.

VERILATOR_ATSIM
   Command to use to invoke Atsim.

VERILATOR_GHDL
   Command to use to invoke GHDL.

VERILATOR_GDB
   Command to use to invoke GDB debugger.

VERILATOR_IVERILOG
   Command to use to invoke Icarus Verilog.

VERILATOR_MAKE
   Command to use to rebuild Verilator and run single test.

VERILATOR_MODELSIM
   Command to use to invoke ModelSim.

VERILATOR_NCVERILOG
   Command to use to invoke ncverilog.

VERILATOR_ROOT
   Standard path to Verilator distribution root; see primary Verilator
   documentation.

VERILATOR_SOLVER
   SMT solver command for constrained randomization; see primary Verilator
   documentation.

VERILATOR_TESTS_SITE
   Used with ``--site``, a colon-separated list of directories with tests
   to be added to testlist.

VERILATOR_VCS
   Command to use to invoke VCS.

VERILATOR_XELAB
   Command to use to invoke XSim xelab

VERILATOR_XVLOG
   Command to use to invoke XSim xvlog


Test Language
=============

This section describes the format of the ``test_regress/t/*.py`` test
language files, executed by `driver.py`.

Test Language Summary
---------------------

For convenience, a summary of the most commonly used features is provided
here, with a reference in a later section. All test files typically have a
call to the ``test.lint`` or ``test.compile`` methods to compile the test.
For run-time tests, this is followed by a call to the ``test.execute``
method. Both of these functions can optionally be provided with arguments
specifying additional options.

If those complete, the script calls ``test.passes`` to increment the count
of successful tests.

The driver.py script assumes by default that the source Verilog file name
matches the test script name. So a test whose driver is ``t/t_mytest.py``
will expect a Verilog source file ``t/t_mytest.v``. This can be changed
using the ``top_filename`` subroutine, for example

::

   test.top_filename = "t/t_myothertest.v"

By default, all tests will run with major simulators (Icarus Verilog, NC,
VCS, ModelSim, etc.) as well as Verilator, to allow results to be compared.
However, if you wish a test only to be used with Verilator, you can use the
following:

::

   test.scenarios('vlt')

Of the many options that can be set through arguments to ``test.compiler``
and ``test.execute``, the following are particularly useful:

``verilator_flags2``
   A list of flags to be passed to verilator when compiling.

``fails``
   Set true to indicate that the compilation or execution is intended to
   fail.

For example, the following would specify that compilation requires two
defines and is expected to fail.

::

   test.compile(
      verilator_flags2 => ["-DSMALL_CLOCK -DGATED_COMMENT"],
      fails = True)

Hints On Writing Tests
----------------------

There is generally no need for the test to create its own main program or
top level shell as the driver creates one automatically, however some tests
require their own C++ or SystemC test harness. This is commonly given the
same name as the test, but with .cpp as suffix
(``test_regress/t/t_EXAMPLE.cpp``). This can be specified as follows:

::

   test.compile(
      make_top_shell=False,
      make_main=False,
      verilator_flags2=["--exe", test.t_dir + "/" + test.name + ".cpp"])

Tests should be self-checking, rather than producing lots of output. If a
test succeeds it should print ``*-* All Finished *-*`` to standard output
and terminate (in Verilog ``$finish``), if not it should just stop (in
Verilog ``$stop``) as that signals an error.

If termination should be triggered from the C++ wrapper, the following code
can be used:

::

   vl_fatal(__FILE__, __LINE__, "dut", "<error message goes here>");
   exit(1);

Where it might be useful for a test to produce output, it should qualify
this with ``TEST_VERBOSE``. For example in Verilog:

::

   `ifdef TEST_VERBOSE
       $write("Conditional generate if MASK [%1d] = %d\n", g, MASK[g]);
   `endif

Or in a hand-written C++ wrapper:

::

   #ifdef TEST_VERBOSE
      std::cout << "Read a=" << a << std::endl;
   #endif

A filename that should be used to check the output results is given with
``expect_filename``. This should not generally be used to decide if a test
has succeeded. However, in the case of tests that are designed to fail at
compile time, it is the only option. For example:

::

   compile(
      fails=True,
      expect_filename=test.golden_filename)

Note ``expect_filename`` strips some debugging information from the logfile
when comparing.


Test Language Compile/Lint/Run Arguments
----------------------------------------

This section describes common arguments to ``test.compile``, ``test.lint``,
and ``test.run``. The full list of arguments can be found by looking at the
``driver.py`` source code.

all_run_flags
   A list of flags to be passed when running the simulator (Verilated model
   or one of the other simulators).

check_finished
   True to indicate successful completion of the test is indicated by the
   string ``*-* All Finished *-*`` being printed on standard output. This
   is the normal way for successful tests to finish.

fails
   True to indicate this step is expected to fail. Tests that are expected
   to fail generally have _bad in their filename.

make_main
   False to disable the automatic creation of a C++ test wrapper (for
   example when a hand-written test wrapper is provided using ``verilator
   --exe``).

make_top_shell
   False to disable the automatic creation of a top level shell to run the
   executable (for example when a hand-written test wrapper is provided
   using ``verilator --exe``).

ms_flags / ms_flags2 / ms_run_flags
   The equivalent of ``v_flags``, ``v_flags2`` and ``all_run_flags``, but
   only for use with the ModelSim simulator.

nc_flags / nc_flags2 / nc_run_flags
   The equivalent of ``v_flags``, ``v_flags2`` and ``all_run_flags``, but
   only for use with the Cadence NC simulator.

iv_flags / iv_flags2 / iv_run_flags
   The equivalent of ``v_flags``, ``v_flags2`` and ``all_run_flags``, but
   only for use with the Icarus Verilog simulator.

v_flags
   A list of standard Verilog simulator flags to be passed to the simulator
   compiler (Verilator or one of the other simulators). This list is create
   by the driver and rarely changed, use ``v_flags2`` instead.

v_flags2
   A list of standard Verilog simulator flags to be passed to the simulator
   compiler (Verilator or one of the other simulators). Unlike ``v_flags``,
   these options may be overridden in some simulation files.

   Similar sets of flags exist for atsim, GHDL, Cadence NC, ModelSim and
   Synopsys VCS.

vcs_flags / vcs_flags2 / vcs_run_flags
   The equivalent of ``v_flags``, ``v_flags2`` and ``all_run_flags``, but
   only for use with the Synopsys VCS simulator.

verilator_flags / verilator_flags2
   The equivalent of ``v_flags`` and ``v_flags2``, but only for use with
   Verilator. If a flag is a standard flag, ``+incdir`` for example, pass
   it with ``v_flags2`` instead.

xsim_flags / xsim_flags2 / xsim_run_flags
   The equivalent of ``v_flags``, ``v_flags2`` and ``all_run_flags``, but
   only for use with the Xilinx XSim simulator.


Version Release Process
=======================

This section documents the process to release a new version. This is not
intended to be run by other than the primary repository admins.

#. If activity requires, announce release pending as GitHub issue.
#. Check all test suites pass:

   - rtlmeter (or wait until overnight cron passes).
   - sv-tests (run manually or wait a few days and check proper version was
     tested).
   - uvm (currently requires private accellera access).
   - verilator-ext-tests: ``make pull && make clean && make test``

#. Check documentation:

   - ``nodist/log_changes # and update Changes``
   - ``make spelling # and check warnings``
   - ``make docs # and check warnings``

#. Prepare release:

   - edit ``Changes`` to update version banner.
   - edit ``configure.ac`` to change version number and "devel" to proper
     ISO-format date.
   - edit ``CMakeLists.txt`` version number.

#. Test and release:

   - ``git commit -am "Version bump"``
   - ``make distclean && autoconf && ./configure && make test``
   - ``make maintainer-dist``

#. Wait for GitHub CI build actions to pass.
#. File announcement issue at verilator-announce, pasting in ``Changes``
   information.
#. Devel release:

   - edit ``Changes``, ``configure.ac``, ``CMakeLists.txt`` to devel and
     devel version number.
   - ``git commit -am "devel release"``
   - ``make distclean && autoconf && ./configure && make test``
   - ``git push``

#. Merge GitHub pull requests tagged ``status:merge-after-release``.
#. Check ``docs/guide/deprecations.rst`` for things due to remove.


Distribution
============

Verilator is free software; you can redistribute it and/or modify it under
the terms of either the GNU Lesser General Public License Version 3 or the
Perl Artistic License Version 2.0.

SPDX-FileCopyrightText: 2003-2026 Wilson Snyder

SPDX-License-Identifier: LGPL-3.0-only OR Artistic-2.0

.. |Logo| image:: https://www.veripool.org/img/verilator_256_200_min.png
