haxe + numba

README.MD

Haxe + numba sample

$ haxe build.hxml

$ python testhx.py
Test.hx:28: 499999700005.0959
Test.hx:29: first calc, 0.20508950000000015
Test.hx:33: 499999700005.0959
Test.hx:34: second calc, 0.05553469999999994

http://numba.pydata.org/numba-doc/latest/index.html

http://numba.pydata.org/numba-doc/latest/user/5minguide.html

https://devblogs.nvidia.com/numba-python-cuda-acceleration/

build.hxml

-cp .
-D analyzer-optimize
-main Test
-python testhx.py
--dce full

Test.hx

import haxe.Timer;
import haxe.Constraints.Function;

@:pythonImport("numba.typed", "List")
extern class List extends Array<Dynamic> {
	public function new();
	public function append(val:Dynamic):Void;
	public function __iter__():Dynamic;
}

@:pythonImport("numba")
extern class Numba {
	static function njit(func:Function):Function;
}

class Test {
	static function main() {
		var arr = new List();
		for (i in 0...1000000)
			arr.append(i + 0.1);
		var arr2 = new List();
		for (i in 0...1000000)
			arr2.append(i + 0.1);

		var jit = Numba.njit(sum);
		var start = Timer.stamp();
		var a = jit(arr, arr2);
		trace(a);
		trace("first calc", Timer.stamp() - start);

		var start = Timer.stamp();
		var a = jit(arr, arr2);
		trace(a);
		trace("second calc", Timer.stamp() - start);
	}

	static function sum(arr:List, arr2:List) {
		var res = 0.;
		var len = arr.length;
		var len2 = arr.length;
		var i = -1;
		var j = -1;
		while (++i < len)
			while (++j < len2)
				res += (arr[i] : Float) + (arr2[j] : Float);
		return res;
	}
}