Fastest way to negate a std::vector

Question

Assume I have a std::vector of double, namely

std::vector<double> MyVec(N); 

Where N is so big that performance matters. Now assume that MyVec is a nontrivial vector (i.e. it is not a vector of zeros, but has been modified by some routine). Now, I need the negated version of the vector: I need -MyVec.

So far, I have been implementing it via

std::transform(MyVec.cbegin(),MyVec.cend(),MyVec.begin(),std::negate<double>()); 

But, really, I do not know if this is something sensible or it is just super naïve from my side.

Am I doing it correctly? Or std::transform is just a super slow routine in this case?

PS: I am using BLAS and LAPACK libraries all the time, but I have not found anything that matches this particular need. However, if there exists such a function in BLAS/LAPACK which is faster than std::transform, I would be glad to know.

Answer 1

#include <vector> #include <algorithm> #include <functional>  void check() {     std::vector<double> MyVec(255);     std::transform(MyVec.cbegin(),MyVec.cend(),MyVec.begin(),std::negate<double>()); } 

This code on https://godbolt.org/ with copile option -O3 generate nice assembly

.L3: [...]   cmp r8, 254   je .L4   movsd xmm0, QWORD PTR [rdi+2032]   xorpd xmm0, XMMWORD PTR .LC0[rip]   movsd QWORD PTR [rdi+2032], xmm0 .L4: 

It's difficult to imagine faster. Your code is already perfect, don't try to outsmart the compiler and use clean C++ code it works almost every times.

Answer 2

Fortunately the data in std::vector is contiguous so you can multiply by -1 using vector intrinsics (using unaligned load/stores and special handing of the possible overflow). Or use ippsMulC_64f/ippsMulC_64f_I from intel's IPP library (you'll struggle to write something faster) which will use the largest vector registers available to your platform: https://software.intel.com/en-us/ipp-dev-reference-mulc

Update: to clear up some confusion in the comments, the full version of Intel IPP is free (although you can pay for support) and comes on Linux, Windows and macOS.