Parallel Programming in Fortran 95 using OpenMP - People

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$Intel(R) Math Kernel Library for Linux* OS User's Guide$

50 3. PRIVATE, SHARED & Co. DYNAMIC : in the previous scheduling method the problem of non homogeneous iterations has been pointed out. In such a situation it turns out to be difficult to forecast the optimal distribution of the iterations among the threads. A solution would be to assign the different blocks of work in a dynamic way: as one thread finishes its piece of work, it gets a new one. This idea is precisely what lies behind the present scheduling method. When SCHEDULE(DYNAMIC,chunk) is specified, the iteration space is divided into pieces of work with a size equal to chunk. If this optional parameter is not given, then a size equal to one iteration is considered. Thereafter, each thread gets one of these pieces of work. When they have finished with their task, they get assigned a new one until no pieces of work are left. The best way to understand the working principle of this scheduling method is by means of an example. In the top part of figure 3.7 12 different pieces of work are shown. The length of each of these blocks represents the amount of computational time required to compute them. These pieces of work are going to be distributed over a team of three threads. The resulting execution schema is shown in the bottom part of figure 3.7. Clearly to see is that each thread gets a new piece or work when it finishes the previous one. GUIDED : the previous dynamic method enhances the capabilities and performance with respect to the static method, but it adds an overhead in computational cost due to the handling and distributing of the different iteration packages. This overhead is reduced as the size of the pieces of work increases, but then a larger non-equilibrium between the different threads is to expect. Another dynamic approach to this problem could be to have pieces of work with decreasing sizes, so that their associated work is smaller and smaller as they are assigned to the different threads. The decreasing law is of exponential nature so that the following pieces of work have half the number of iterations as the previous ones. This is what the present scheduling method does. The optional parameter chunk specifies the smallest number of iterations grouped into one piece of work. As before, the last piece of work may have a smaller number of iterations than specified in chunk in order to cover completely the iteration space. The number of pieces of work with equal size and the largest size in use are OpenMPimplementation dependent. In figure 3.8 an example of the resulting pieces of work is shown. The associated do-loop has the following form: !$OMP DO SCHEDULE(GUIDED,256) do i = 1, 10230 ... enddo !$OMP END DO
3.2. Other clauses 51 Pieces of work 1 2 3 4 5 6 7 8 9 10 11 12 thread 0 thread 1 thread 2 1 2 3 6 4 5 E xecution parallel region 7 8 9 10 11 12 ❄ Figure 3.7: Graphical representation of the example explaining the working principle of the SCHEDULE(DYNAMIC,chunk) clause.
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Parallel Programming in Fortran 95
Page 4 and 5: ii CONTENTS 3.1.4 FIRSTPRIVATE(list
Page 6 and 7: 2 1. OpenMP Fortran Application Pro
Page 14 and 15: 10 2. OpenMP constructs • Work-sh
Page 16 and 17: 12 2. OpenMP constructs real(8) ::
Page 18 and 19: 14 2. OpenMP constructs do k = 1, 1
Page 20 and 21: 16 2. OpenMP constructs !$OMP SECTI
Page 22 and 23: 18 2. OpenMP constructs thread 0 th
Page 24 and 25: 20 2. OpenMP constructs • If the
Page 26 and 27: 22 2. OpenMP constructs 2.3 Synchro
Page 32 and 33: 28 2. OpenMP constructs !$OMP DO do
Page 34 and 35: 30 2. OpenMP constructs 2.3.6 !$OMP
Page 36 and 37: 32 2. OpenMP constructs !$OMP END D
Page 38 and 39: 34 2. OpenMP constructs Shared memo
Page 40 and 41: 36 2. OpenMP constructs
Page 42 and 43: 38 3. PRIVATE, SHARED & Co. Shared
Page 46 and 47: 42 3. PRIVATE, SHARED & Co. In this
Page 50 and 51: 46 3. PRIVATE, SHARED & Co. Operato
Page 52 and 53: 48 3. PRIVATE, SHARED & Co. it is n
Page 56 and 57: 52 3. PRIVATE, SHARED & Co. Pieces
Page 58 and 59: 54 3. PRIVATE, SHARED & Co.
Page 60 and 61: 56 4. The OpenMP run-time library T
Page 62 and 63: 58 4. The OpenMP run-time library d
Page 64 and 65: 60 4. The OpenMP run-time library 4
Page 66 and 67: 62 4. The OpenMP run-time library T
Page 68 and 69: 64 4. The OpenMP run-time library I
Page 70 and 71: 66 4. The OpenMP run-time library 4
Page 72 and 73: 68 4. The OpenMP run-time library i
Page 74 and 75: 70 5. The environment variables 5.1
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