[Numpy-discussion] about SIMD (SSE2 & SSE3)
Sat Nov 6 18:46:57 CDT 2010
the java program takes 3 hours to read data, after read the data into
memory, it takes 4 hours to process/calculate somthing on all these data.
The data is the sale data which contains both promoted sale and non-promoted
sale, the program needs to predict the non-promoted sale: so input data is
a serial of promoted sale and non-promoted sale, the output is a serial of
non-promoted sale. e.g
day 1,2,3 have the non-promoted sales, day 4 have the promoted sales, day
5,6,7 have the non-promted sales, the output for day 1~7 are all
non-promoted sales. During the process, we might need to sum all the data
for day 1~7, is this what you called " elementwise addition,
multiplication", which can't be SIMDed in numpy?
On Sun, Nov 7, 2010 at 3:46 AM, David Warde-Farley <
> On 2010-11-06, at 8:51 AM, qihua wu wrote:
> > I used the following command to install the numpy to enable the SSE3
> > numpy-1.5.1rc1-win32-superpack-python3.1.exe /arch sse3
> > Then how can I know whether numpy is running with SSE or not?
> As far as I know, the only thing that uses SSE/SSE2/SSE3 would be BLAS
> operations. Things like elementwise addition, multiplication, etc. are not
> implemented to take advantage of vectorized machine instructions, at least
> not yet, unless the C compiler is aggressively optimizing and doing some
> loop unrolling which I sort of doubt.
> > I have a program to process the data from sql server using java to
> process 600M rows, it takes 7 hours to complete, about 4 hours is eating the
> cpu. I am wondering whether I can port the java to numpy to cut the 4 hours
> to 2hours or even less by enabling the SSE3. Any comment?
> It's not clear that crunching data from an SQL database would be any faster
> with NumPy. It really depends on the specifics of your problem.
> NumPy-Discussion mailing list
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