PyPy faster than C on a carefully crafted example

Good day everyone.

Recent round of optimizations, especially loop invariant code motion has been very good for small to medium examples. There is work ongoing to make them scale to larger ones, however there are few examples worth showing how well they perform. This one following example, besides getting benefits from loop invariants, also shows a difference between static and dynamic compilation. In fact, after applying all the optimizations C does, only a JIT can use the extra bit of runtime information to run even faster.

The example is as follows. First Python. I create two files, x.py:

def add(a, b):
  return a + b

And y.py:

from x import add

def main():
    i = 0
    a = 0.0
    while i < 1000000000:
        a += 1.0
        add(a, a)
        i += 1

main()

For C, x.c:

double add(double a, double b)
{
  return a + b;
}

and y.c:

double add(double a, double b);

int main()
{
  int i = 0;
  double a = 0;
  while (i < 1000000000) {
    a += 1.0;
    add(a, a);
    i++;
  }
}

Results?

• 1.97s - PyPy
• 3.07s - C

Compilation options:

• PyPy trunk (386ed41eae0c), running pypy-c y.py
• C - gcc -O3 (GCC 4.4.5 shipped with Ubuntu Maverick)

Hence, PyPy 50% faster than C on this carefully crafted example. The reason is obvious - static compiler can't inline across file boundaries. In C, you can somehow circumvent that, however, it wouldn't anyway work with shared libraries. In Python however, even when the whole import system is completely dynamic, the JIT can dynamically find out what can be inlined. That example would work equally well for Java and other decent JITs, it's however good to see we work in the same space :-)

Cheers,
fijal

EDIT: Updated GCC version