Matlab Parallel loop: parfor

If we are in the situations with simple large loop even in a single CPU, using parfor is better than ordinary loop in some cases... it will enhance the efficiency dramatically. When the loop number is small, for will definitely win as the parallel command will cost time. But when the N is large, parloop will run. A good thing is to make the test to optimize the program.

s1 = 0.0; s2 = 0.0;  for j = 1:100  N = 10000;  clear A tic;   parfor i = 1:N    A(i) = exp(log(i)); end final = toc ;    s1 = s1 + final;  clear A tic;  for i = 1:N    A(i)= exp(log(i)); end final = toc;    s2 = s2+ final;  end    fprintf(1,'parfor loop runs %12.6f seconds\n',s1);   fprintf(1,'normal loop runs %12.6f seconds\n',s2); 

N=10000;

parfor loop runs 2.149823 seconds

normal loop runs 17.243006 seconds

N = 100;

parfor loop runs 0.296427 seconds

normal loop runs 0.017778 seconds

Of course, the results will be difference when we use the matlabpool open to make actual parallel run. It will depend on the overhead....

对中国CPI简单评论

1） CPI存在外部因数， 但外部大宗商品指数上涨部分依赖于中国内部因数。

2） CPI内部因数主要有三：

a） 超发货币政策导致热钱汹涌到每一角落， 因此各种商品都存在大幅涨价空间。

b) 房价，物流价格等等已经和世界接轨，也必然导致其他商品价格， 包括食品价格与世界接轨，甚至引领世界潮流。

c) 中国收入分配不均，底层广大商品包括劳动力成本被各种因数长时间人为压低。 中国很大一部分GDP增长（包括未来）来自于被压低的成本回归， 即平均购买力变现。