[SciPy-dev] scipy.weave versus simple C++.
eric
eric at scipy.org
Fri Jan 11 09:17:50 CST 2002
Hey Prabhu,
Thanks for the comparison. This kind of benchmarking is very helpful for
figuring out what work still need to be done.
Send me your code, and I'll either make an inline() version of it, or try
and figure out how we can do the slow stuff with blitz().
eric
----- Original Message -----
From: "Prabhu Ramachandran" <prabhu at aero.iitm.ernet.in>
To: <scipy-dev at scipy.org>
Sent: Friday, January 11, 2002 10:44 AM
Subject: [SciPy-dev] scipy.weave versus simple C++.
> hi,
>
> I've been checking out Python, Numeric, Weave recently for serious
> numeric computations. I've started off with a very easy sample
> problem that translates well to Numeric expressions. The results are
> very interesting.
>
> I'm comparing weave.blitz with simple C++ code (no classes or virtual
> functions etc, just plain arrays and structs.) The problem is to
> solve the Laplace equation on a square grid with a given boundary
> condition using a four point average. Here is what I found.
>
> The core loop in C++ (I use double for all the computations).
>
> double **u = g->u; // g is a simple structure.
>
> for (int i=1; i<nx-1; ++i) {
> for (int j=1; j<ny-1; ++j) {
> tmp = u[i][j];
> u[i][j] = ((u[i-1][j] + u[i+1][j])*dy2 +
> (u[i][j-1] + u[i][j+1])*dx2)*dnr_inv;
> err += SQR(u[i][j] - tmp); // SQR is a simple inline function.
> }
> return sqrt(err);
>
> The same core code in Python is next. I've labelled different
> sections for convenience.
>
> u = self.grid.u # a (500x500 NumPy array).
> # Step 1
> self.grid.old_u = u.copy() # yes, memory inefficient.
>
> # Step 2
> expr = "u[1:-1, 1:-1] = ((u[0:-2, 1:-1] + u[2:, 1:-1])*dy2 + "\
> "(u[1:-1,0:-2] + u[1:-1, 2:])*dx2)*dnr_inv"
> scipy.weave.blitz(expr, check_size=0)
>
> # Step 3
> v = (u - self.grid.old_u).flat
> return sqrt(dot(v,v))
>
>
> I compiled the c++ code with the -O3 flag. I then time the programs
> for a 500x500 grid for 100 iterations.
>
> As it stands, here is the comparison.
>
> Weave.Blitz/Python: 10.68 s
> Numeric/Python: 31.80 s
> C++: 2.71 s
> Best/Ratio: ~4.0
>
> For other runsI get different numbers but the ratio seems set on about
> 4. This is pretty darned good. Please remember that the Python code
> does the 100 iterations in pure Python along with a few while/if
> conditions to check for the error and number of iterations and other
> things . Only the core loop has been optimized with weave/Numeric.
>
> If you look at the Python code you will notice that Step 1 and Step 3
> are pretty bad steps. Just for the sake of comparison I remove Step 1
> alone. I recompute the C++ code again just to take care in case of
> other processes on my system etc.
>
> Weave.Blitz/Python: 7.0 s
> C++: 2.47 s
> Ratio: 2.83
>
> You can see some definite improvement. Now I go further and replace
> Step 3 with 'return 1.0'. Here are the results:
>
> Weave.Blitz/Python: 2.17 s
> C++: 2.41 s
> Ratio: 0.90 s
>
> This is an amazing result and shows why blitz++ and consequently
> weave.blitz is so cool -- its faster than simple C/C++. I also inter
> changed the order of the for loops in the C code and it only gets
> slower (so thisis the best possible way to do it I guess).
>
> This shows that weave holds tremendous promise. However, I have a few
> questions/issues.
>
> (1) Is there a better way to speed up the copy (step 1) and remove
> the old_u array? Numeric is nice but not perfect.
>
> (2) I tried using blitz to speed up the error computation (Step 3)
> and thecopy (step 1) but no go. I couldnt get it to work.
>
> (3) Whats the best way to deal with stuff that belongs in the
> innermost loop (like the error computation that I do) ? Would
> weave.inline do the job? I'd really like an inline example that
> does something like my C++ for loop above using Numeric or Python
> arrays. It would be very illustrative.
>
> All in all weave looks to be *very* promising!! I hope it grows and
> gets even better. :)
>
> Please let me know if you want to look at my code. Also, let me know
> if I'mdoing something brain dead here.
>
> prabhu
>
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