Numpy dot product very slow using ints

Question

sorry for so many questions. I am running Mac OSX 10.6 on Intel core 2 Duo. I am running some benchmarks for my research and I have run into another thing that baffles me. <

Answer 1

Using int vs float data types causes different code paths to be executed:

The stack trace for float looks like this:

(gdb) backtr
#0  0x007865a0 in dgemm_ () from /usr/lib/libblas.so.3gf
#1  0x007559d5 in cblas_dgemm () from /usr/lib/libblas.so.3gf
#2  0x00744108 in dotblas_matrixproduct (__NPY_UNUSED_TAGGEDdummy=0x0, args=(, ), 
kwargs=0x0) at numpy/core/blasdot/_dotblas.c:798
#3  0x08088ba1 in PyEval_EvalFrameEx ()
...

..while the stack trace for int looks like this:

(gdb) backtr
#0  LONG_dot (ip1=0xb700a280 "\t", is1=4, ip2=0xb737dc64 "\a", is2=4000, op=0xb6496fc4 "", n=1000, __NPY_UNUSED_TAGGEDignore=0x85fa960)
at numpy/core/src/multiarray/arraytypes.c.src:3076
#1  0x00659d9d in PyArray_MatrixProduct2 (op1=, op2=, out=0x0)
at numpy/core/src/multiarray/multiarraymodule.c:847
#2  0x00742b93 in dotblas_matrixproduct (__NPY_UNUSED_TAGGEDdummy=0x0, args=(, ), 
kwargs=0x0) at numpy/core/blasdot/_dotblas.c:254
#3  0x08088ba1 in PyEval_EvalFrameEx ()
...

Both calls lead to dotblas_matrixproduct, but it appears that the float call stays in the BLAS library (probably accessing some well-optimized code), while the int call gets kicked back out to numpy's PyArray_MatrixProduct2.

So this is either a bug or BLAS just doesn't support integer types in matrixproduct (which seems rather unlikely).

Here's an easy and inexpensive workaround:

af = a.astype(float)
np.dot(af, af).astype(int)