![python vectorize for loop python vectorize for loop](https://vectorified.com/image/vector-loops-5.jpg)
This difference doesn’t matter that much in Fortran, where do loops are still fast, but it matters a lot in interpreted languages like matlab and python, where a for loop might be tens or hundreds of times slower than using a numpy array statement.īut the key thing that I think is causing some of your confusion is that those two different uses of the word “vectorization” are completely different. For example, some people refer to c(:) = a(:) + b(:) as “vectorized”, where an equivalent do loop (like in your example) would be “non-vectorized”. Vectorization can mean using array statements instead of loops. Vectorization can mean the use of SIMD instructions like AVX, which allow the CPU to perform multiple operations in the time it would otherwise take to perform one, as you know.
Python vectorize for loop manual#
I also noticed an author wrote a guide for optimization Fortran code, is what he said in the manual still true? general_fortran_optimization_guide_manual.pdfĢ85.26 One other thing that is probably contributing to the confusion here, is that there are two completely different things that are sometimes referred to as “vectorization”. It seem the compiler are doing very good job or highly optimized in just doing the do loops. It seems in many case using a do loop is faster than writing things like a(:)=b(:)+c(:).
![python vectorize for loop python vectorize for loop](https://i.ytimg.com/vi/yxg0z0pVEyU/maxresdefault.jpg)
However, for something more complicated, like a(:) = log(b(:) + c(:)*exp(a(:))*ln(b(:))) I mean is vectorization is only for those very basic operatons like, op = + - * / a(:) = b(:) op c(:)
![python vectorize for loop python vectorize for loop](https://learn.yantramayaa.com/wp-content/uploads/2022/06/Python-Training-Program-LearnWithYantraMayaa-1-600x1067.png)
Or slightly complicated things like, example4Įxample4 integer, parameter :: r8=selected_real_kind(15,9)Ĭ(:) = exp(a(:)) + log(b(:)) + exp(a(:)) * log(b(:))
Python vectorize for loop code#
Therefore actually example2 is faster than example1.īut I mean in principle both example1 and 2 should be the same speed right? In example2, the compiler should be smart enough to vectorize the code correspondingly.įinally, in the below code, example3, is there any vectorization?Įxample3 integer, parameter :: r8=selected_real_kind(15,9) I notice that the compiler in example1 may complain that the array are not aligned or something. For AVX2 it is with 256 bit register or something, The above code is like doing c(1:4) = a(1:4) + b(1:4) in one clock cycle or something, and 4 is because 256/64=4.īut why the hell many times I found that actually doing the loop is faster? see example 2,Įxample2 integer, parameter :: r8=selected_real_kind(15,9)
Python vectorize for loop 64 Bit#
The above code should be vectorized,right? The real is real 8 type, it should took 64 bit in the ram. I know in the following code, sorry for the sloppy code, see example1Įxample1 integer, parameter :: r8=selected_real_kind(15,9) Actually all the below examples are using Intel Fortran, I am not sure if gfortran behave simialrly but I guess so. Like, all the below code are using -O3 -xHost (or -Ofast -march=native in gfortran) optimization, and we are using a AVX2 CPU (256 bit register or something whatever). But still not very clear what is vectorization.Ĭould anyone give some small examples? Thanks in advance! I know there are many materials I can find on internet, google. I always feel not exactly clear what is vectorization, never really and fully understand it.