PR #7902 uncovered a pre-existing bug in multi-threaded scheduling,
where we can end up with an un-ordered R-W hazard in the MTask graph,
resulting in non-deterministic runtime behaviour.
This is extremely hard to actually trigger on a small example, so using
ThreadSanitizer to flag it, which can identify the race reliably.
In this patch:
- Add configure and `verilator --get-supported TSAN` to check if the
configured compiler supports ThreadSanitizer
- Add a --tsan option to the test driver.py which builds the test with
thread sanitizer (similar idea to --gdbsim).
- Add a tests.enable_tsan() method to allow turning on TSan in the test
Python file.
- Add a suppressions file that waives TSan errors in the runtime library
- Finally add `t_sched_hybrid_hazard` that demonstrates the data race
triggered after #7902. This is currently expected failing, fix later.
With the suppression, there are 17 vltmt tests failing due races in the
generated code. (Using `driver.py --vltmt --tsan --quiet -j0`)
Rewrite module inlining decision to be based on a bipartite Module/Cell
graph, similar to V3InlineCFuncs. Preserved all old heuristics, but
added 2 new ones:
- If a module, and all the sub-hierarchy below it, is less than 10% the
total flattened size of the design, then flatten the contents of that
module (but the module itself is not necessarily inlined).
- If the flattened size of all instances of a module is less than 20% of
the total flattened size of the design, then inline all instances of
that module.
These are both relative to the total size of the design, so they
auto-scale with complexity. The net effect is that large shared
instances are preserved, but their contents are flattened out. E.g. in a
multi-core CPU this would keep the cores non-inlined but flatten out
most everything else. This still enables V3Combining and sharing those
later, but avoids potentially big overheads e.g. with small widely used
library modules.
Empirically this yields less generated C++ than the previous version
(due to removing lots of small functions), and can improve performance
10-20% while still having meaningful combining relative to the size of
the design.
Kornel Uriasz
Nick Brereton
Wilson Snyder
Igor Zaworski
Jeffrey Song
Yilou Wang
Geza Lore
Yogish Sekhar
Artur Bieniek
Adam Kostrzewski
Jaeuk Lee
Patrick Creighton
Jakub Michalski
Matthew Ballance
Pawel Klopotek
Eryk Szpotański
Pawel Kojma
github action
Sergey Chusov
Joshua Leahy