Data races and the limits of ThreadSanitizer in C and Go

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To gain confidence in code we write tests. But when you add concurrency to the mix the bugs become nondeterministic. To gain additional confidence in concurrent code we might enable a race detector that tells us that a race happened in some piece of code during a run. This is useful because our tests might still have passed even though a race happened. The existence of the race, whether our tests otherwise fail or not, means that a latent bug absolutely exists.

Most major language implementations that have any race detector (Clang, GCC, Go, Swift, OCaml) use LLVM’s ThreadSanitizer. ThreadSanitizer (TSan) is not well documented. It has gone through three major iterations and while you can find the algorithm for TSan version two (released in 2012), the author of TSan suggests we just read the source to understand version three (released in 2021). Perhaps someone will contribute new docs.

There is a rich history of algorithms for detecting data races like Eraser (which influenced TSan version one), FastTrack (has ideas in common with TSan versions two and three), RaceTrack by Microsoft, and so on. Each algorithm has its own limitations, as TSan does too. And Clang, GCC, and Go do emit generic entry points for race detector libraries, even if TSan is the only one in serious use today.

In this article we’ll walk through the basics of data races. Then we’ll implement an idealized interpreter in Python for multi-threaded C code alongside FastTrack-style vector clocks to show how TSan roughly works. Then we’ll show how architecture choices make it possible to overload TSan in a few dimensions, causing it to miss obvious data races. For example, TSan cannot reliably report data races while crossing a 255 total thread boundary. This is not a particularly rare situation, considering web services implemented in a language like Go with one goroutine per request.

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