Which is more expensive ? Post: 302131762

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Top Forums Programming Which is more expensive ? Post 302131762 by Perderabo on Monday 13th of August 2007 03:39:02 PM

08-13-2007

Administrator Emeritus

I would code your first snippet but compile with an optimizer. These days optimizers will unroll loops if unrolling is advantageous. That particular loop is not a real great candidate for unrolling anyway. A better candidate would be:

for(i=0; i<100; i++) A[i]=0;

Most superscalar cpus can execute:
A[i]=0;
A[i+1]=0;
A[i+2]=0;
simultaneously. How deep it can go depends on the cpu and that's why leaving unrolling to an optimizer is a good idea. The optimizer should know the target cpu. But your case involved a system call which is different. You're only saving some loop overhead.

Apparently, if you explicitly unroll a loop when it is not advantageous, most optimizers will not reroll the loop. At least this was the case circa 1998 when my copy of "High Performance Computing" was published. If you have that book, see chapter 8, "Loop Optimizations" and chapter 9, "Understanding Parallelism". This is still a great book and it's not just for Fortran programmers.

Anyway, if you are not in control of which fd's might be open, you need to to loop up to OPEN_MAX closing them. High fd's might have been opened and then setrlimit() called lower to the max fd.

Perderabo

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fann_get_cascade_candidate_change_fraction

FANN_GET_CASCADE_CANDIDATE_CHANGE_FRACTION(3)				 1			     FANN_GET_CASCADE_CANDIDATE_CHANGE_FRACTION(3)

fann_get_cascade_candidate_change_fraction - Returns the cascade candidate change fraction

SYNOPSIS

       float fann_get_cascade_candidate_change_fraction (resource  $ann)

DESCRIPTION

	The cascade candidate change fraction is a number between 0 and 1 determining how large a fraction the fann_get_MSE(3) value should change
       within fann_get_cascade_candidate_stagnation_epochs(3) during training of the candidate neurons, in order for the training not to stagnate.
       If the training stagnates, the training of the candidate neurons will be ended and the best candidate will be selected.

	It  means  that if the MSE does not change by a fraction of fann_get_cascade_candidate_change_fraction(3) during a period of fann_get_cas-
       cade_candidate_stagnation_epochs(3), the training of the candidate neurons is stopped because the training has stagnated.

	If the cascade candidate change fraction is low, the candidate neurons will be trained more and if the	fraction  is  high  they  will	be
       trained less.

	The default cascade candidate change fraction is 0.01, which is equalent to a 1% change in MSE.

PARAMETERS

	      o $ann
		-Neural network resource.

RETURN VALUES

	The cascade candidate change fraction, or FALSE on error.

SEE ALSO

       fann_set_cascade_candidate_change_fraction(3), fann_get_MSE(3), fann_get_cascade_candidate_stagnation_epochs(3).

PHP Documentation Group 									     FANN_GET_CASCADE_CANDIDATE_CHANGE_FRACTION(3)