# MATLAB's JIT Engine

32 views (last 30 days)
Royi Avital on 2 Sep 2012
Edited: Royi Avital on 2 Jun 2019
Do you think MATLAB's JIT engine is oudated? It does look so.
Look at Julia Language: http://julialang.org/
What do you think?
I hope Mathworks will surprise us in the next version.

Jan on 2 Sep 2012
I find this code for the comparison between Julia and Matlab:
function n = parseintperf(t)
for i = 1:t
n = randi([0,2^32-1], 1, 'uint32');
s = dec2hex(n);
m = hex2dec(s);
assert(m == n);
end
This takes in the provided test a time of 179.23 milliseconds.
It would be fair to omit the assert() and use more efficient conversions:
function n = parseintperf(t)
for i = 1:t
n = randi([0,2^32-1], 1, 'uint32');
s = sprintf('%8X', n);
m = sscanf(s, '%X');
end
44.18 milliseconds (Matlab 2009a/64, Win7).
I did not find similar mistakes in the other tests.

per isakson on 2 Sep 2012
Edited: per isakson on 3 Sep 2012
The following comparison might not be interesting from a CS point of view, but it is to a typical Matlab user.
Sure, the performance of Julia is impressive and The Mathworks might have some catch-up to do. However, backward compatibility; they must not break our code.
With Matlab vectorization is a powerful measure to achieve speed. Julia doesn't support vectorization.
I did the following test on R2012a, 64bit, Windows7.
>> Julia_test_parseintperf
matlab, parse_int, 2189.9
matlab, parse_int_vec, 18.1
where the result is in milliseconds and
function Julia_test_parseintperf
julia_timeit( 'parse_int' , @parseintperf , 1e4 )
julia_timeit( 'parse_int_vec', @parseintperf_vec, 1e4 )
end
function n = parseintperf(t)
for ii = 1:t
n = randi([0,2^32-1],1,'uint32');
s = dec2hex(n);
m = hex2dec(s);
assert(m == n);
end
end
function n = parseintperf_vec( t )
n = randi( [0,2^32-1], t, 1, 'uint32' );
s = dec2hex( n );
m = hex2dec( s );
assert( all( m == n ) );
end
@Jan, I run this test with and without assert and the profiler and found that assert does not add much to the time. That function is improved - it seems. [See below.]
I had to rename the the function, timeit, used in their benchmark.
.
--- In response to comment 1 ---
I have added two functions proposed by Jan and run the test with the assert included
>> Julia_test_parseintperf
matlab, parse_int, 1698.9
matlab, parse_int_vec, 18.0
matlab, parse_int_jan, 394.6
matlab, parse_int_jan_vec, 12.0
and the result from a second run with the assert commented out
>> Julia_test_parseintperf
matlab, parse_int, 1666.8
matlab, parse_int_vec, 17.9
matlab, parse_int_jan, 359.6
matlab, parse_int_jan_vec, 12.0
where I have added these two functions proposed by Jan
function n = parseintperf_jan(t)
for ii = 1:t
n = randi([0,2^32-1], 1, 'uint32');
s = sprintf('%8X', n);
m = sscanf(s, '%8X');
% assert(m == n);
end
end
function n = parseintperf_jan_vec(t)
n = randi([0,2^32-1], t, 1, 'uint32');
s = sprintf('%8X ', n);
m = sscanf(s, '%8X');
% assert( all(m == n));
end
@Jan, my comment above regarding assert might have been wishful thinking. I was looking for order of magnitudes and was happy to have the asserts.
Some months ago I listen to presentation of Julia on Youtube (Stanford?). One thing I remember is that their understanding of "JIT-theory" influenced he design of the Julia language. With Matlab is more of an afterthought. However, lets hope Julia will influence the "Matlab accelerator" to improve. (You just indicated a way to improve the speed of dec2hex/hex2dec by a third.)
Yes, the JavaScript benchmark results are remarkable. JavaScript goes back to Netscape? I just saw this:
Numeric Javascript is a library for numerical computations in
Javascript. You can write Javascript programs that manipulate
matrices, solve linear problems, find eigenvalues and solve
optimization problems in the browser using Numeric Workshop or
use Numeric Javascript outside the browser in any Javascript
environment.
amin ya on 2 Jun 2019
Julia natively supports vectorization and SIMD. SIMD is much faster than parfor which slows down the code instead of speeding it up

Daniel Shub on 3 Sep 2012
I am not sure it is possible to separate the MATLAB JIT accelerator from the rest of MATLAB. MATLAB is fast enough for most of what I do. In the rare cases I need something faster I tend to write C code. I do think the performance of MATLAB could be improved with a major overhaul similar to the move from Python 2 to 3 in which concerns for backwards compatibility were ignored. But just as I still use Python 2, I would stick with the older MATLAB.

Jan on 3 Sep 2012
When I ask my favorite search engine for "Matlab compile auto parallelize" I get a bunch of links about tools, which compile Matlab code with an automatic parallelization. Some of them are quite comfortable and produce efficient programs. But the drawback is that there can be two different sources of problems in case of bugs: The M-code and the compiler, and interferences of both.
Royi Avital on 29 Jun 2015
Could you elaborate on that?

Royi Avital on 12 Jul 2014
Another look on the subject:
And they got much faster.
I really hope Mathworks will do something.
IF MATLAB one day will be as fast as C, or at least close to it as close as Julia gets, it might become an application development language.

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