Multicore Programming Using the ParC Language (Undergraduate Topics in Computer Science)
Yosi Ben-Asher
Language: English
Pages: 277
ISBN: 1447121635
Format: PDF / Kindle (mobi) / ePub
Multicore Programming Using the ParC Language discusses the principles of practical parallel programming using shared memory on multicore machines. It uses a simple yet powerful parallel dialect of C called ParC as the basic programming language. Designed to be used in an introductory course in parallel programming and covering basic and advanced concepts of parallel programming via ParC examples, the book combines a mixture of research directions, covering issues in parallel operating systems, and compilation techniques relevant for shared memory and multicore machines.
Multicore Programming Using the ParC Language provides a firm basis for the ‘delicate art’ of creating efficient parallel programs. Students can exercise parallel programming using a simulation software, which is portable on PC/Unix multicore computers, to gain experience without requiring specialist hardware. Students can also help to cement their learning by completing the great many challenging and exciting exercises which accompany each chapter.
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divides the wall into sections of size , where one brick is left as a gap between every two adjacent workers. In the first stage each worker constructs a pyramid of wall height H. Each row that a worker puts down must be smaller by one brick than the previous one. The total number of bricks in a pyramid is therefore: The total number of bricks that are needed to fill in the gap is . Therefore, the expected parallel time for a round wall with N=L⋅H bricks and P workers is: This is also the
languages, scoping is often augmented with explicit declarations that variables are either private or shared. We find that explicit declarations are unnecessary, as the scoping naturally leads to a rich selection of sharing patterns. Since ParC allows parallel constructs to be freely nested inside other parallel constructions it naturally creates nested patterns of shared/private variables. As each iteration of a parfor/parblock creates a new thread we obtain multiple set of stacks for which the
to a shared variable. Typically, large variables like 2D-pictures or matrices are best processed in parallel as partitioned data-structure. In this case each thread will mainly compute and update its local part, but also will access neighboring parts to get updated. In general the assumption that for parallel machines it is always possible to partition shared data such that the memory references made by one thread to its part will always cost as local references is only approximation. In reality,
variable x is shared between several threads and the compiler replaces all references to x with references to a register in a loop, x will no longer be visible to the MESI protocol. In such a case, updates made to x during a loop would not be visible to other threads. Volatile variables in C can be used to force the compiler not to use registers to update x, in case this is important. In the case of ParC, the compilation method of ParC makes all accesses to shared variables through a pointer pass
algorithm: Monotonicity: The statement if ((M[i][k]) && (M[k][j])) M[i][j] = 1; can never produce wrong results, regardless of any execution order. Local estimate: Since in a synchronous run logn iterations are enough, logn can be used for the local estimate. Halting criterion: If there are no indexes i,j,k such that i is not connected to j, but i can be connected to j via k, then all connections have been computed. This criterion can be computed in parallel. The program is shown in Fig. 7.4.
