Benchmarks

Overview

Here is a quick overview of the execution time of Koffi calls on three benchmarks, where it is compared to a theoretical ideal FFI implementation (approximated with pre-compiled static N-API glue code):

• The first benchmark is based on rand() calls

• The second benchmark is based on atoi() calls

• The third benchmark is based on Raylib

These results are detailed and explained below, and compared to node-ffi/node-ffi-napi.

Linux x86_64

The results presented below were measured on my x86_64 Linux machine (Intel® Core™ i5-4460).

rand results

This test is based around repeated calls to a simple standard C function rand, and has three implementations:

• the first one is the reference, it calls rand through an N-API module, and is close to the theoretical limit of a perfect (no overhead) Node.js > C FFI implementation (pre-compiled static glue code)

• the second one calls rand through Koffi

• the third one uses the official Node.js FFI implementation, node-ffi-napi

Benchmark	Iteration time	Relative performance	Overhead
rand_napi	842 ns	x1.00	(ref)
rand_koffi	1114 ns	x0.76	+32%
rand_node_ffi	44845 ns	x0.02	+5224%

Because rand is a pretty small function, the FFI overhead is clearly visible.

atoi results

This test is similar to the rand one, but it is based on atoi, which takes a string parameter. Javascript (V8) to C string conversion is relatively slow and heavy.

Benchmark	Iteration time	Relative performance	Overhead
atoi_napi	921 ns	x1.00	(ref)
atoi_koffi	1357 ns	x0.68	+47%
atoi_node_ffi	152550 ns	x0.006	+16472%

Because atoi is a pretty small function, the FFI overhead is clearly visible.

Raylib results

This benchmark uses the CPU-based image drawing functions in Raylib. The calls are much heavier than in previous benchmarks, thus the FFI overhead is reduced. In this implementation, Koffi is compared to:

• Baseline: Full C++ version of the code (no JS)

• node-raylib: This is a native wrapper implemented with N-API

Benchmark	Iteration time	Relative performance	Overhead
raylib_cc	215.7 µs	x1.20	-17%
raylib_node_raylib	258.9 µs	x1.00	(ref)
raylib_koffi	311.6 µs	x0.83	+20%
raylib_node_ffi	928.4 µs	x0.28	+259%

Windows x86_64

The results presented below were measured on my x86_64 Windows machine (Intel® Core™ i5-4460).

rand results

This test is based around repeated calls to a simple standard C function rand, and has three implementations:

• the first one is the reference, it calls rand through an N-API module, and is close to the theoretical limit of a perfect (no overhead) Node.js > C FFI implementation (pre-compiled static glue code)

• the second one calls rand through Koffi

• the third one uses the official Node.js FFI implementation, node-ffi-napi

Benchmark	Iteration time	Relative performance	Overhead
rand_napi	964 ns	x1.00	(ref)
rand_koffi	1274 ns	x0.76	+32%
rand_node_ffi	42300 ns	x0.02	+4289%

Because rand is a pretty small function, the FFI overhead is clearly visible.

atoi results

This test is similar to the rand one, but it is based on atoi, which takes a string parameter. Javascript (V8) to C string conversion is relatively slow and heavy.

The results below were measured on my x86_64 Windows machine (Intel® Core™ i5-4460):

Benchmark	Iteration time	Relative performance	Overhead
atoi_napi	1415 ns	x1.00	(ref)
atoi_koffi	2193 ns	x0.65	+55%
atoi_node_ffi	168300 ns	x0.008	+11792%

Because atoi is a pretty small function, the FFI overhead is clearly visible.

Raylib results

This benchmark uses the CPU-based image drawing functions in Raylib. The calls are much heavier than in the atoi benchmark, thus the FFI overhead is reduced. In this implementation, Koffi is compared to:

• node-raylib (baseline): This is a native wrapper implemented with N-API

• raylib_cc: C++ implementation of the benchmark, without any Javascript

Benchmark	Iteration time	Relative performance	Overhead
raylib_cc	211.8 µs	x1.25	-20%
raylib_node_raylib	264.4 µs	x1.00	(ref)
raylib_koffi	318.9 µs	x0.83	+21%
raylib_node_ffi	1146.2 µs	x0.23	+334%

Please note that in order to get fair numbers for raylib_node_raylib, it was recompiled with clang-cl before running the benchmark with the following commands:

cd node_modules\raylib
rmdir /S /Q bin build
npx cmake-js compile -t ClangCL

Running benchmarks

Open a console, go to koffi/benchmark and run ../../cnoke/cnoke.js (or node ..\..\cnoke\cnoke.js on Windows) before doing anything else.

Please note that all benchmark results are made with Clang-built binaries.

cd koffi/benchmark
node ../../cnoke/cnoke.js --prefer-clang

Once everything is built and ready, run:

node benchmark.js