Den 10.02.2011 16:29, skrev eat: > One would expect sum to outperform dot with a clear marginal. Does > there exixts any 'tricks' to increase the performance of sum?
I see that others have ansvered already. The ufunc np.sum is not going going to beat np.dot. You are racing the heavy machinery of NumPy (array iterators, type chekcs, bound checks, etc.) against level-3 BLAS routine DGEMM, the most heavily optimized numerical kernel ever written. Also beware that computation is much cheaper than memory access. Although DGEMM does more arithmetics, and even is O(N3) in that respect, it is always faster except for very sparse arrays. If you need fast loops, you can always write your own Fortran or C, and even insert OpenMP pragmas. But don't expect that to beat optimized high-level BLAS kernels by any margin. The first chapters of "Numerical Methods in Fortran 90" might be worth reading. It deals with several of these issues, including dimensional expansion, which is important for writing fast numerical code -- but not intuitively obvious. "I expect this to be faster because it does less work" is a fundamental misconception in numerical computing. Whatever cause less traffic on the memory BUS (the real bottleneck) will almost always be faster, regardless of the amount of work done by the CPU. A good advice is to use high-level BLAS whenever you can. The only exception, as mentioned, is when matrices get very sparse. Sturla _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
