xesite/blog/ffi-ing-golang-from-nim-for-fun-and-profit-2015-12-20.markdown

title	date	series	tags
FFI-ing Go from Nim for Fun and Profit	2015-12-20	howto	go nim

As a side effect of Go 1.5, the compiler and runtime recently gained the ability to compile code and run it as FFI code running in a C namespace. This means that you can take any Go function that expresses its types and the like as something compatible with C and use it from C, Haskell, Nim, Luajit, Python, anywhere. There are some unique benefits and disadvantages to this however.

A Simple Example

Consider the following Go file add.go:

package main

import "C"

//export add
func add(a, b int) int {
    return a + b
}

func main() {}

This just exposes a function add that takes some pair of C integers and then returns their sum.

We can build it with:

$ go build -buildmode=c-shared -o libsum.so add.go

And then test it like this:

$ python
>>> from ctypes import cdll
>>> a = cdll.LoadLibrary("./libsum.so")
>>> print a.add(4,5)
9

And there we go, a Go function exposed and usable in Python. However now we need to consider the overhead when switching contexts from your app to your Go code. To minimize context switches, I am going to write the rest of the code in this post in Nim because it natively compiles down to C and has some of the best C FFI I have used.

We can now define libsum.nim as:

proc add*(a, b: cint): cint {.importc, dynlib: "./libsum.so", noSideEffect.}

when isMainModule:
  echo add(4,5)

Which when ran:

$ nim c -r libsum
Hint: system [Processing]
Hint: libsum [Processing]
CC: libsum
CC: system
Hint:  [Link]
Hint: operation successful (9859 lines compiled; 1.650 sec total; 14.148MB; Debug Build) [SuccessX]
9

Good, we can consistently add 4 and 5 and get 9 back.

Now we can benchmark this by using the times.cpuTime() proc:

# test.nim

import
  times,
  libsum

let beginning = cpuTime()

echo "Starting Go FFI at " & $beginning

for i in countup(1, 100_000):
  let myi = i.cint
  discard libsum.add(myi, myi)

let endTime = cpuTime()

echo "Ended at " & $endTime
echo "Total: " & $(endTime - beginning)

$ nim c -r test
Hint: system [Processing]
Hint: test [Processing]
Hint: times [Processing]
Hint: strutils [Processing]
Hint: parseutils [Processing]
Hint: libsum [Processing]
CC: test
CC: system
CC: times
CC: strutils
CC: parseutils
CC: libsum
Hint:  [Link]
Hint: operation successful (13455 lines compiled; 1.384 sec total; 21.220MB; Debug Build) [SuccessX]
Starting Go FFI at 0.000845
Ended at 0.131602
Total: 0.130757

Yikes. This takes 0.13 seconds to do the actual computation of every number i in the range of 0 through 100,000. I ran this for a few hundred times and found out that it was actually consistently scoring between 0.12 and 0.2 seconds. Obviously this cannot be a universal hammer and the FFI is very expensive.

For comparison, consider the following C library code:

// libcsum.c
#include "libcsum.h"

int add(int a, int b) {
  return a+b;
}

// libcsum.h
extern int add(int a, int b);

# libcsum.nim
proc add*(a, b: cint): cint {.importc, dynlib: "./libcsum.so", noSideEffect.}

when isMainModule:
  echo add(4, 5)

and then have test.nim use the C library for comparison:

# test.nim

import
  times,
  libcsum,
  libsum

let beginning = cpuTime()

echo "Starting Go FFI at " & $beginning

for i in countup(1, 100_000):
  let myi = i.cint
  discard libsum.add(myi, myi)

let endTime = cpuTime()

echo "Ended at " & $endTime
echo "Total: " & $(endTime - beginning)

let cpre = cpuTime()
echo "starting C FFI at " & $cpre

for i in countup(1, 100_000):
  let myi = i.cint
  discard libcsum.add(myi, myi)

let cpost = cpuTime()

echo "Ended at " & $cpost
echo "Total: " & $(cpost - cpre)

Then run it:

➜  nim c -r test
Hint: system [Processing]
Hint: test [Processing]
Hint: times [Processing]
Hint: strutils [Processing]
Hint: parseutils [Processing]
Hint: libcsum [Processing]
Hint: libsum [Processing]
CC: test
CC: system
CC: times
CC: strutils
CC: parseutils
CC: libcsum
CC: libsum
Hint:  [Link]
Hint: operation successful (13455 lines compiled; 0.972 sec total; 21.220MB; Debug Build) [SuccessX]
Starting Go FFI at 0.00094
Ended at 0.119729
Total: 0.118789

starting C FFI at 0.119866
Ended at 0.12206
Total: 0.002194000000000002

Interesting. The Go library must be doing more per instance than just adding the two numbers and continuing about. Since we have two near identical test programs for each version of the library, let's strace it and see if there is anything that can be optimized. The Go one and the C one are both very simple and it looks like the Go runtime is adding the overhead.

Let's see what happens if we do that big loop in Go:

// add.go

//export addmanytimes
func addmanytimes() {
    for i := 0; i < 100000; i++ {
        add(i, i)
    }
}

Then amend libsum.nim for this function:

proc addmanytimes*() {.importc, dynlib: "./libsum.so".}

And finally test it:

# test.nim

echo "Doing the entire loop in Go. Starting at " & $beforeGo

libsum.addmanytimes()

let afterGo = cpuTime()

echo "Ended at " & $afterGo
echo "Total: " & $(afterGo - beforeGo) & " seconds"

Which yields:

Doing the entire loop in Go. Starting at 0.119757
Ended at 0.119846
Total: 8.899999999999186e-05 seconds

Porting the C library to have a similar function would likely yield similar results, as would putting the entire loop inside Nim. Even though this trick was only demonstrated with Nim and Python, it will work with nearly any language that can convert to/from C types for FFI. Given the large number of languages that do have such an interface though, it seems unlikely that there will be any language in common use that you cannot write to bind to Go code. Just be careful and offload as much of it as you can to Go. The FFI barrier really hurts.

---

This post's code is available here.