GopherCon 2018 - Adventures in Cgo Performance

By Keegan Carruthers-Smith for the GopherCon Liveblog on August 29, 2018

Presenter: Mr. Sean T. Allen

Liveblogger: Keegan Carruthers-Smith

Covers hard-won knowledge about using cgo in performance sensitive code including: ways in which cgo makes interoperation with other languages difficult, how you can work around common sources of performance and scaling problems, and an issue with the Go runtime that can't be worked around.


An abstract, slides and all links mentioned in the talk are at

The overhead of calling from Go into C has a high overhead (171ns/op). It is even worse from C to Go (1-2ms). The talk covers techniques to mitigate the impact, namely batching, "big old map", sharding and avoiding lock contention.


Sean has lots of experience in FFI over a very long time. For example he has even written a bridge between Java <-> Perl. At Wallaroo Labs he needed to call into Pony from cgo, and vice versa. Wallaroo users would write go and call this API, which was expected to be very high performance. This lead Sean down the road of cgo performance.


Cgo allows you to call "C" from go. It also allows you to call go from "C". The latter is not as commonly used. To Sean's eye, cgo is not really an FFI. Technically it is, but he cgo has the follow drawbacks unlike previous FFI's he has used:

  • cgo does not have minimal overhead.
  • cgo does not make it straightforward to pass memory back and forth.

Cgo is not go. There is an awful lot of things that make go nice, like the toolchain allowing static binaries, cross compiling, data race finding, etc. With cgo you get none of that, hence cgo is not go.

Cgo performance: It's complicated. Here is a quick rundown of the cost of calling a function. In C it is a few ns. C code from python, also single digit ns. But C and go live in different universes, so a lot of work has to be done to cross over. For example goroutines have growable stacks, and also has additional housekeeping to pass control of the underlying thread. This is all overhead. There is a great blog post by Cockroach Labs called Cost of complexity of cgo. It contains some interesting timings:

  • cgo 10,000,000 runs avg 171ns/op
  • go 2,000,000,000 runs avg 1.83ns/op

It is even worse the opposite direction. For calling Go from "C" he observed the following timings: On an AWS machine 1-2MS/op. His 2014 Macbook Pro 5-6ms/op.

runtime/proc.go line 1771 contains a very interesting TODO. Essentially on every call go does a bunch of computation which it could store, but it throws it away. It assumes that CGO calls will be rare and that the cost of the computations is not that bad. However, in his case it is common and given the runtime has changed so much, the cost of throwing the data away is likely a lot worse than when the comment was written.


Batch your CGO calls. You should know this going into it, since it can fundementally affect your design. Additionally once you cross the boundary, try to do as much on the other side as you can. So for go => "C" do as much as you can in a single "C" call. Similarly for "C" => go do as much as you can in a single go call. Even more so since the overhead is much higher.

Pointers into go controlled memory are not allowed to be held by "C" code. A "copying" garbage collectors will move objects in memory as part of the GC cycle. This means you can't hold onto a pointer and assume it will stay pointing at a valid object. Go does not do this! However, go1.6 they decided they may want to do this in the future, so added the rule for not allowing you to do this.

But you can work around this if you really need to. The "Big old map". Keep an index into a huge map maintained by go. As a bonus this also includes a reference to the values in the map, so you don't have to worry about the GC clearing out the objects if only the C code needs to reference them.

The problem with a big old map is you need to be thread safe, which means you need a RWMutex to protect the map access. This leads to serious performance problems due to lock contention as you scale up the number of cores.

So the next thing to do when you have problems with "big old map" contention, is to add sharding. The new sync.Map structure uses the same idea. You create N "big old maps", then given the key you just pick key % N for the map to use. Picking the shard doesn't require a lock, so the lock contention moves to each shard.

You still need a way to generate the keys for the map. The lock in our id generator is a source of contention. You can use the atomic pkg to solve this. eg id := atomc.AddUint64(&, 1). Even better is if you use something intrinsic to the value for the key. If you do that though you need to ensure your sharding function works psuedorandomnly with the ID values you use.