LuaJIT 2.1?

[pygy]

Do you plan to migrate to the 2.1 branch? It is faster than v2.0.x, and AFAIK stable enough to be used in production at CloudFlare.

[franko]

Hi,

actually I was just waiting the first stable release of the 2.1 branch but, as you suggest, it is probably ok to migrate with the next release of GSL Shell. I've actually a lot of minor changes to include and a new release is a good thing.

Otherwise what about a new gsl shell's branch in github to integrate luajit 2.1 ?

[pygy]

Otherwise what about a new gsl shell's branch in github to integrate luajit 2.1 ?

Why not.

AFAICT, the parse.c and Makefile modifications work as is in 2.1.

I'll also have to send you a patch for compiling on OS X 10.8

[franko]

Now there is a v2.1 branch in GSL Shell's repository

https://github.com/franko/gsl-shell/tree/master-lj2.1

The merge was very easy thanks to the power of git :-) and everything seems to work just fine.

Francesco

[pygy]

Cool :-)

The Julia guys are about to add the LuaJIT/GSL Shell benchmarks you wrote on their home page. I'll point them to the LJ 2.1 branch.

LuaJIT v2.1 is 10 times faster than v2.0 for parseint, but a bit slower for mandel (but, in both cases, it still beats the hell out of C :-).

The pure JavaScript (V8) implementation of rand_mat_stat is faster than its GSL Shell counterpart, which relies on BLAS, as do the C, Julia and Fortran benchmars. The latter three are also faster than LuaJIT/GSL Shell. Maybe you're not using the same BLAS?

LuaJIT is ~10 times slower than C for rand_mat_mul, but faster than JS.

Check here for the results on my machine: JuliaLang/julia@9a57b99#commitcomment-5996981

Edit: note also that quicksort can be made faster by switching to a FFI array.

[franko]

The benchmark results looks good to me.

I agree that there are some odd things. I already noticed in past that Julia was faster in rand_mat_mul but I cannot tell the reason. The only things I can suggest is to ensure that openblas is actually used for gsl shell. For me the over speed should be given by the underlying BLAS implementation.

Otherwise I would not be too picky about this benchmark results and I'm afraid I don't have enough time to further investigate the problem.

In any case I will be glad if they include lua/gsl-shell in their benchmark page. Thank you for your help about that.

[pygy]

How can I set the BLAS version?

On my machine, the GSL-based rand_mat_mul is ~10% faster than a straight port of the JavaScript code to Lua:

local darray = ffi.typeof("double[?]")

local function randd(n)
    local v, r
    v = darray(n)
    r = rng.new('rand')

    for i = 0, n-1 do
        v[i] = r:get()
    end

    return v
end

-- Transpose mxn matrix.
local function mattransp(A, m, n)
    local T = darray(m * n)

    for i = 0, m - 1 do
        for j = 0, n-1 do
            T[j * m + i] = A[i * n + j]
        end
    end
    return T
end


local function matmul(A,B,m,l,n)
    local C, total
    C = darray(m*n)
    -- Transpose B to take advantage of memory locality.
    B = mattransp(B,l,n)

    for i = 0, m - 1 do
        for j = 0, n - 1 do
            total = 0

            for k = 0, l - 1 do
                total = total + A[i*l+k]*B[j*l+k]
            end

            C[i*n+j] = total
        end
    end

    return C
end


local function randmatmulLJ(n)
    local A, B
    A = randd(n*n)
    B = randd(n*n)

    return matmul(A, B, n, n, n)
end

timeit(|| randmatmul(1000), "rand_mat_mul")      --> 1129.19
timeit(|| randmatmulLJ(1000), "rand_mat_mul_LJ") --> 1255.42

BTW:

$ node perf.js
...
javascript,rand_mat_mul,2933

:-)

[franko]

To check the BLAS library you have to "ldd" the executable and see to which file libblas.so points to by using "ls -l ".

I'm now wondering if Julia is faster because it does transpose the matrix before the multiplication just like JS is doing. In principle I should do some tests with dgemm with and without transpose like in the JS code but unfortunately I don't have time to work on that.

[pygy]

There's no ldd on OS X, otool -L does the trick.

$ otool -L gsl-shell | grep blas
    /usr/local/lib/libgslcblas.0.dylib (compatibility version 1.0.0, current version 1.0.0)
$ ls -l /usr/local/lib/libgslcblas.0.dylib
lrwxr-xr-x  1 pygy  staff  42 Apr 12 23:57 /usr/local/lib/libgslcblas.0.dylib -> ../Cellar/gsl/1.16/lib/libgslcblas.0.dylib

The GSL, as installed by brew relies on the default libgslcblas. I've tried to redirect the symlink to a freshly compiled OpenBLAS, but it complains about version issues (1.0.0 required, 0.0.0 found). The same goes for the Julia BLAS.

I'm also trying to build the gsl by hand, but I don't know how to tell it to use another BLAS.

[pygy]

I got it to compile with OpenBLAS (by adding the proper paths and options in the GSL Shell Makefile).

randommatmul is now as fast as C/Julia :-)

It may be nice to add the possibility to customize LIBS and LDFLAGS in makeconfig.

[franko]

Good :-)

Actually the libraries are supposed to be configurable using the file "makepackages" but may be this is not very intuitive.

On linux "makepackages" links with any "blas" library (using GSL_LIBS) provided by the system and thus openblas is not required. It is possible to modify the default makefile to links explicitly to openblas but I'm not sure this is a good idea.

May be a warning can be shown during compile time if the gslcblas library is used since this latter is really slow.

Suggestion & patches are welcome.

[pygy]

makepackage is probably fine... I tend to explore code rather than read the docs (too often, there are none), and I though that makeconfig was were users were supposed to tweak things.

OS X also provides a fast BLAS, I'll look how to link to it.

[pygy]

I found the system BLAS, which is even faster than OpenBLAS, but I don't know if it is found at the same path for all OS X versions.

Edit: actally, adding -lBLAS to the GSL_LIBS does the trick, without adding any path to the linker (which actually makes sense).