Hello, I am writing in C + + in visual studio 2012. Please help me to modify the function Creating_Computation that she performed on the xeon phi 5110p.

# Please help me to modify the function

## Please help me to modify the function

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made that the code was run on xeon phi, but the code is very slow, please help to optimize, tell me how. I just do not send to addresses)

## Attachments:

Attachment | Size |
---|---|

Download xeon-phi-test.cpp | 3.44 KB |

I am not quite sure where this code came from. Can you point me to the original source?

As far as why this version isn't running very fast, the inner loop is not vectorizing as written. To get peak performation from the coprocessor, the code should both vectorize and multithread.

Please let your super programmers) on my example will show how to do this))) Here's how to speed up the cycle, just show and I'll jump to the ceiling with joy))))

# pragma offload target (mic) in (Ti, Pr: length (Number_Of_Lines)) out (Se, TA, FL: length (Number_Of_Lines))

{

# pragma omp parallel for

for (int Index_Computation = 0; Index_Computation <Number_Of_Lines; Index_Computation + +)

{

int Fl_L = 0, Pr_Open = Pr [Index_Computation], Pr_Reals = 0;

int X = Pr_Open + Flo_left, Y = Pr_Open - Flo_right;

for (int ii = Index_Computation; ii <Number_Of_Lines; ii + +)

{

Pr_Reals = Pr [ii];

if ((Pr_Open - Pr_Reals)> Fl_L) Fl_L = Pr_Open - Pr_Reals;

if (Pr_Reals> = X)

{

Se [Index_Computation] = 1;

TA [Index_Computation] = Ti [ii];

FL [Index_Computation] = Fl_L;

break;

}

if (Pr_Reals <= Y)

{

Se [Index_Computation] = 0;

TA [Index_Computation] = Ti [ii];

FL [Index_Computation] = Fl_L;

break;

}

if (ii == Number_Of_Lines-1)

{

Se [Index_Computation] = -1;

TA [Index_Computation] = -1;

FL [Index_Computation] = -1;

}

}

}

}

how to speed up the cycle? show by my example vectorization

int SIZE = 10000000;

int a1 [SIZE]; here the data for calculations (array of computer memory)

int a2 [SIZE]; here the data for calculations (array of computer memory)

int a3 [SIZE]; takes an array of values (array of computer memory)

int a4 [SIZE]; takes an array of values (array of computer memory)

int a5 [SIZE]; takes an array of values (array of computer memory)

for (inti = 0; i <SIZE; i) {

a3 [i] = a1 [i] + a2 [i];

a4 [i] = a1 [i] - a2 [i];

a5 [i] = a1 [i] * a2 [i];

}

In your code, xeon-phi-test.cpp, you use clock() to time your results. This is generally not a good routine for timing parallel code where you are more concerned with elapsed time than with the number of clock cycles your program used. (In Linux, people often use gettimeofday().)

But regardless -

The inner loop in xeon-phi-test.cpp does not vectorize on either the host or the coprocessor and after staring at it for a while, I'm afraid I don't see a way to get it to vectorize. There are two problems. One is the break used in the if tests inside the loop. This gets in the way of the compiler being able to determine the trip count for the loop. The other is the "if ( ( Pr_Open - Pr[ii] ) > Fl_L ) Fl_L = Pr_Open - Pr[ii];" which introduces a vector dependency - we don't know if Pr_Open -Pr[ii] is greater than Fl_L until we know if Pr_Open-Pr[ii-1] was greater than Fl_L. Others wiser than myself are welcome to look at this and see if they have a solution.

As far as why the code runs more slowly on the coprocessor than on the host, even though the code fails to vectorize on both systems, there are two things going on. First, the way time is being measured in xeon-phi-test.cpp, the time to move the data back and forth to the coprocessor is being included in the timing for the coprocessor. This is only a small effect in this case. Second, and much more significant, the performance penalty for not vectorizing is greater for the coprocessor than for the host. The coprocessor gets a big part of its permormance from using those 512 wide vector units.

As far as the simple example you give:

for (inti = 0; i <SIZE; i++) { a3 [i] = a1 [i] + a2 [i]; a4 [i] = a1 [i] - a2 [i]; a5 [i] = a1 [i] * a2 [i]; }

The compiler can vectorize this as long as it has some assurance that the vectors are well behaved. (That there is no aliasing that might introduce a vector dependency.) This is true regardless of whether the code is being compiled for the host or for the coprocessor.

There is a good article on vectorization (http://software.intel.com/en-us/articles/vectorization-essential) which lots of good links that you might like to read for a better understanding of vectorization.