aza/APP/nexus-remote/node_modules/koffi/doc/pages/benchmarks.md

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 (AMD Ryzen™ 5 2600).

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	569 ns	x1.00	(ref)
rand_koffi	855 ns	x0.67	+50%
rand_node_ffi	58730 ns	x0.010	+10228%

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	1039 ns	x1.00	(ref)
atoi_koffi	1642 ns	x0.63	+58%
atoi_node_ffi	164790 ns	x0.006	+15767%

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	17.5 µs	x1.34	-25%
raylib_node_raylib	23.4 µs	x1.00	(ref)
raylib_koffi	28.8 µs	x0.81	+23%
raylib_node_ffi	103.9 µs	x0.23	+344%

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	859 ns	x1.00	(ref)
rand_koffi	1352 ns	x0.64	+57%
rand_node_ffi	35640 ns	x0.02	+4048%

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	1336 ns	x1.00	(ref)
atoi_koffi	2440 ns	x0.55	+83%
atoi_node_ffi	136890 ns	x0.010	+10144%

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	18.2 µs	x1.50	-33%
raylib_node_raylib	27.3 µs	x1.00	(ref)
raylib_koffi	29.8 µs	x0.92	+9%
raylib_node_ffi	96.3 µs	x0.28	+253%

Running benchmarks

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

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

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

Once everything is built and ready, run:

node benchmark.js