Generate fast implementations of mathematical expressions.
Here is a practical, editable use case - a good starting point to look at the basics.
Think like this:
Matrix multiplication a * b: Each row of a with each column of b
...write code like you think, without caring about performance...
# Python c = a * b
c = [[ sum(x*y for x,y in zip(ra,cb)) for cb in zip(*b) ] for ra in a ]// JavaScript
function matmulrows_zip( a, b )
{
return a.map( function (ra) {
return zip.apply( null, b ).map( function (cb) {
return sum(
zip( ra, cb )
.map( function (xy) { return xy[ 0 ] * xy[ 1 ]; } )
);
} );
} );
}
// --> Well encapsulated code, but slow due to function call overhead....and let flatorize transform the above into very fast JavaScript code:
// Generated "flatorized" code == factorized + flattened
//
// - factorized: avoid repeating computations.
// - flattened: no function call.
function anonymous(a, b) {
/* function (a,b) { return symbol_matmulrows_zip_gen( a, b, 3, 4, 2 ); } */
var
_0 = a[0]
, _1 = _0[0]
, _2 = _0[1]
, _3 = _0[2]
, _4 = _0[3]
, _5 = a[1]
, _6 = _5[0]
, _7 = _5[1]
, _8 = _5[2]
, _9 = _5[3]
, _a = a[2]
, _b = _a[0]
, _c = _a[1]
, _d = _a[2]
, _e = _a[3]
, _f = b[0]
, _g = _f[0]
, _h = _f[1]
, _i = b[1]
, _j = _i[0]
, _k = _i[1]
, _l = b[2]
, _m = _l[0]
, _n = _l[1]
, _o = b[3]
, _p = _o[0]
, _q = _o[1]
;
return [ [ (_1 * _g) + (_2 * _j) + (_3 * _m) + (_4 * _p), (_1 * _h) + (_2 * _k) + (_3 * _n) + (_4 * _q) ], [ (_6 * _g) + (_7 * _j) + (_8 * _m) + (_9 * _p), (_6 * _h) + (_7 * _k) + (_8 * _n) + (_9 * _q) ], [ (_b * _g) + (_c * _j) + (_d * _m) + (_e * _p), (_b * _h) + (_c * _k) + (_d * _n) + (_e * _q) ] ];
}
Speedups exceed +1000% in many cases, including matrix multiplication and the Discrete Fourier Transform (full results).
A plugin permits to generate even faster JavaScript code using TypedArray, in an asm.js-compatible way. For usage explanations, see the live page.
A plugin permits to generate fast C code as well. For usage explanations, see the live page.
A plugin permits to generate fast D code as well. For usage explanations, see the live page.
For more details read the article:
- download this repository and open ./index.html
- or visit the live page including extensive speed tests on the Discrete Fourier Transform
- you can also run the unit tests and speed tests yourself
See also the slides and video from the Budapest 2014 nodebp/bpjs meetup.