verilator/docs/internals.adoc

= Verilator Internals Documentation
:toc: right

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== 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 many 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), `ifsp` 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.

`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.

==== `AstNVisitor`

The passes are implemented by AST visitor classes.  These are implemented
by subclasses of the abstract class, `AstNVisitor`. 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 to output them in
https://www.graphviz.org[GraphViz] 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 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`.

=== 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 not prohibitive 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 are 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 simply 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 simply 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 `InstrCountCostVisitor`.  This
class is perhaps the most fragile piece of the multithread implementation.
It's easy to have a bug where you count something cheap (eg. 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-threads` 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 turns out to be 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 multi-thread 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.

===== 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 multi-threaded
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 a 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.

== Coding Conventions

=== Compiler Version and C++11

Verilator supports GCC 4.4.7 and newer.  GCC 4.4.7 does not support C++11,
therefore C++11 is generally not required.  Exceptions may be made to
require C++11 for features that are only practical with C++11,
e.g. threads.

=== 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:

* All files should contain the magic header to ensure standard indentation:
+
   // -*- mode: C++; c-file-style: "cc-mode" -*-
+
This sets indentation to the `cc-mode` defaults.  (Verilator predates a
CC-mode change of several years ago which overrides the defaults with GNU
style indentation; the `c-set-style` undoes that.)

* Use "mixedCapsSymbols" instead of "underlined_symbols".

* Uas a "p" suffix on variables that are pointers, e.g. "nodep".

* Comment every member variable.

Indentation is automatically maintained with "make clang-format" (using
clang-format version 10.0.0).  For those manually formatting:

* Use 4 spaces per level, and no tabs.

* Use 2 spaces between the end of source and the beginning of a comment.

* Use 1 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).

=== The `astgen` Script

Some of the code implementing passes is extremely repetitive, and must be
implemented for each sub-class of `AstNode`. However, while repetitive,
there is more variability than can be handled in C++ macros.

In Verilator this is implemented by using a Perl script, `astgen` to
pre-process the C++ code. For example in `V3Const.cpp` this 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 `AstNVisitor`. All
derived classes of `AstNode` implement the `accept` method, which takes as
argument a reference to an instance or a `AstNVisitor` derived class and
applies the visit method of the `AstNVisitor` 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 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 `user5()` methods, or as a pointer (of type `AstNUser`)
using the `user1p()` through `user5p()` 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 more messy 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

`AstNVisitor` provides a set of iterators to facilitate walking over the
tree. Each operates on the current `AstNVisitor` 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

For an overview of how to write a test see the BUGS section of the
Verilator primary manual.

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 http://systemc.org

* Parallel::Forker from CPAN to run tests in parallel, you can install this
with e.g. "sudo cpan install Parallel::Forker".

* vcddiff to find differences in VCD outputs. See the readme at
https://github.com/veripool/vcddiff

* Cmake for build paths that use it.

* Bit::Vector to test vgen.pl

=== Controlling the Test Driver

Test drivers are written in PERL. All invoke the main test driver script,
which can provide detailed help on all the features available when writing
a test driver.

   test_regress/driver.pl --help

For convenience, a summary of the most commonly used features is provided
here. All drivers require a call to `compile` subroutine to compile the
test. For run-time tests, this is followed by a call to the `execute`
subroutine. Both of these functions can optionally be provided with a hash
table as argument specifying additional options.

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

   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:

   scenarios(vlt => 1);

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

`verilator_flags2`::

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

`fails`::

Set to 1 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.

   compile (
      verilator_flags2 => ["-DSMALL_CLOCK -DGATED_COMMENT"],
      fails => 1,
      );

=== Regression Testing for Developers

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

`--enable-ccwarn`::

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.

When enabling the long tests, some additional PERL modules are needed,
which you can install using cpan.

   cpan install Unix::Processors

There are some traps to avoid when running regression tests

* When checking the MANIFEST, the test will barf 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.

=== 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.pl

=== Continuous Integration

Verilator has a https://travis-ci.com/verilator/verilator[Travis CI environment]
which automatically tests the master branch for test failures on new commits. It
also runs a daily cron job to validate all of the tests against different OS and
compiler versions.

Developers can connect Travis CI to their GitHub account so that the CI
environment can check their branches too by doing the following:

* Go to https://github.com/marketplace/travis-ci and follow the prompts

* Only the Open Source (FREE) version of Travis CI is required

* Under a Travis CI project click More options > Settings in order to set up a
cron job on a particular branch

=== 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 http://lcamtuf.coredump.cx/afl/[American fuzzy lop]
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

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

=== .dot Output

Dot files are dumps of internal graphs in
https://www.graphviz.org[Graphviz] 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 can be more useful with large graphs.

For dynamic graph viewing consider
http://zvtm.sourceforge.net/zgrviewer.html[ZGRViewer].  If you know of
better viewers let us know; ZGRViewer isn't great for large graphs.

=== .tree Output

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

     NETLIST 0x90fb00 <e1> {a0}
    1: MODULE 0x912b20 <e8822> {a8}  top  L2 [P]
   *1:2: VAR 0x91a780 <e74#> {a22} @dt=0xa2e640(w32)  out_wide [O] WIRE
    1:2:1: BASICDTYPE 0xa2e640 <e2149> {e24} @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.

[cols="20%,80%"]
|===

|`1:2:` | indicates the hierarchy of the `VAR` is the `op2p` pointer under
the `MODULE`, which in turn is the `op1p` pointer under the `NETLIST`

|`VAR` | is the AstNodeType.

| `0x91a780` | is the address of this node.

| `<e74>` | means the 74th edit to the netlist was the last modification to
this node.

| `{a22}` | indicates this node is related to line 22 in the source filename
"a", where "a" is the first file read, "z" the 26th, and "aa" the 27th.

| `@dt=0x...` | indicates the address of the data type this node contains.

| `w32` | indicates the width is 32 bits.

| `out_wide` | is the name of the node, in this case the name of the variable.

| `[O]` |  are flags which vary with the type of node, in this case 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.

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 (which typically means 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...user5p, 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` it 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.

=== Debugging with GDB

The test_regress/driver.pl 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.pl --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 nodep->dumpGdb()  # aliased to "pn" in src/.gdbinit
   pnt nodep
   # or: call nodep->dumpTreeGdb()  # aliased to "pnt" in src/.gdbinit

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()
   ...

== Adding a New Feature

Generally what would you do to add a new feature?

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

. Make a testcase in the test_regress/t/t_EXAMPLE format, see << /TESTING >>.

. If grammar changes are needed, look at the git version of VerilogPerl's
src/VParseGrammar.y, as this grammar supports the full SystemVerilog
language and has a lot of back-and-forth with Verilator's grammar.  Copy
the appropriate rules to src/verilog.y and modify the productions.

. If a new Ast type is needed, add it to V3AstNodes.h. Follow the convention
described above about the AstNode type hierarchy.

. Now you can run "test_regress/t/t_{new testcase}.pl --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.

. 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.

== Distribution

Copyright 2008-2020 by Wilson Snyder.  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-License-Identifier: LGPL-3.0-only OR Artistic-2.0