Thank David, 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 < warde...@iro.umontreal.ca> wrote:
> 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. > > David > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion >
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