By Steven Brawer (Auth.)
Contents: Preface; creation; Tiny Fortran; and working method versions; strategies, Shared reminiscence and straightforward Parallel courses; simple Parallel Programming concepts; obstacles and Race stipulations; creation to Scheduling-Nested Loops; Overcoming facts Dependencies; Scheduling precis; Linear Recurrence Relations--Backward Dependencies; functionality Tuning; Discrete occasion, Discrete Time Simulation; a few purposes; Semaphores and occasions; Programming undertaking. Appendixes. Index. this is often the 1st functional consultant to parallel programming written for the functions programmer without event in parallel programming and no formal laptop technological know-how education
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Contents: Preface; creation; Tiny Fortran; and working procedure versions; approaches, Shared reminiscence and easy Parallel courses; uncomplicated Parallel Programming options; obstacles and Race stipulations; advent to Scheduling-Nested Loops; Overcoming facts Dependencies; Scheduling precis; Linear Recurrence Relations--Backward Dependencies; functionality Tuning; Discrete occasion, Discrete Time Simulation; a few functions; Semaphores and occasions; Programming undertaking.
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A schematic illustration of such a situation is shown in Fig. 3-9, where a single program V has been broken up into two programs, each running on its own processor. In this case, processors 1 and 2 are running program c, while processor 3 is running program a and processor 4 is running b. Program d is idle in Fig. 3-9. The following program fragment, which multiplies two arrays, a and b, to produce a single number, is a typical job which could benefit from parallel processing. real sum,a(1000),b(1000) integer n,i 1 sum = 0.
Eq. , using only sum and suml)? (b) Why does it not matter that a ( i ) is not shared? Answer: (a) One could write Program 4-7 without using sumO. In the ifbranch for process 0, we could have set sum = sum + a ( i ) Then, in the final summation, carried out by process 0, we could have set sum = sum + suml (b) It does not matter whether a ( i ) is shared or not because a ( i ) is not altered. Each process needs only to read the values of a ( i ), not to write new values. Thus, whether the processes get the values from their own copies of a ( i ), or from shared values, does not make any difference.
However, the processes would still look like Fig. 4-2. The nature of the processes is independent of the hardware available to execute them. This is why this book stresses the process and not the number of processors. It is also for this reason that parallel programming can be practiced on a uniprocessor. There is no performance advantage to running a parallel program on a uniprocessor, because both processes would have to share a single processor. In fact, execution time would be longer because of the overhead of creating new processes and because of the interprocess communication required.
Introduction to Parallel Programming by Steven Brawer (Auth.)