matlab for-loop矢量化

Question

我正在寻找适当的矢量化matlab函数以消除多线程for循环和增益速度。

size(A) = N -by- N，其中30 <= N <= 60

1e4 <= numIter <= 1e6

function val=permApproxStochSquare(A,numIter) 
%// A  ... input square non-negative matrix 
%// numIter ... number of interations 

N=size(A,1); 

alpha=zeros(numIter,1); 
for curIter=1:numIter 
    U=randn(N,N); 
    B=U.*sqrt(A); 
    alpha(curIter)=det(B)^2; 
end 

val=mean(alpha); 
end 

Answer 1

总结在注释的代码的两个版本稍微提高性能的讨论：

使用从评论的多个想法，代码需要大约1/3更少的时间：

N=size(A,1); 
%precompute sqrt(A) 
sA=sqrt(A); 
alpha=zeros(numIter,1); 
parfor curIter=1:numIter 
    %vectorizing rand did not improve the performance because it increased communitcation when combined with parfor 
    U=randn(N,N); 
    B=U.*sA; 
    alpha(curIter)=det(B); 
end 
%moved calculation out of the loop to vectorize 
val=mean(alpha.^2); 

另一种方法，向量化尽可能使用for循环，只做小improvemens到perfrmance：

N=size(A,1); 
%precompute sqrt(A) 
sA=sqrt(A); 
alpha=zeros(numIter,1); 
%using a for, a vectorized rand outside the loop is faster. 
U=randn(N,N,numIter); 
B=bsxfun(@times,U,sA); 
for curIter=1:numIter 
    alpha(curIter)=det(B(:,:,curIter)); 
end 
val=mean(alpha.^2);