Search results
Results from the WOW.Com Content Network
Loop-level parallelism is a form of parallelism in software programming that is concerned with extracting parallel tasks from loops. The opportunity for loop-level parallelism often arises in computing programs where data is stored in random access data structures .
In this example, code block 1 shows loop-dependent dependence between statement S2 iteration i and statement S1 iteration i-1. This is to say that statement S2 cannot proceed until statement S1 in the previous iteration finishes. Code block 2 show loop independent dependence between statements S1 and S2 in the same iteration.
We can exploit data parallelism in the preceding code to execute it faster as the arithmetic is loop independent. Parallelization of the matrix multiplication code is achieved by using OpenMP . An OpenMP directive, "omp parallel for" instructs the compiler to execute the code in the for loop in parallel.
DOACROSS parallelism is a parallelization technique used to perform Loop-level parallelism by utilizing synchronisation primitives between statements in a loop. This technique is used when a loop cannot be fully parallelized by DOALL parallelism due to data dependencies between loop iterations, typically loop-carried dependencies.
A trivial example involves serving static data. It would take very little effort to have many processing units produce the same set of bits. Indeed, the famous Hello World problem could easily be parallelized with few programming considerations or computational costs. Some examples of embarrassingly parallel problems include: Monte Carlo ...
In the example below, there is an output dependency between instructions 3 and 1 — changing the ordering of instructions in this example will change the final value of A, thus these instructions cannot be executed in parallel. 1. B = 3 2. A = B + 1 3. B = 7 As with anti-dependencies, output dependencies are name dependencies. That is, they ...
A typical example is the parallel DO loop, where different processors work on separate parts of the arrays involved in the loop. At the end of the loop, execution is synchronized (with soft- or hard-barriers [ 6 ] ), and processors (processes) continue to the next available section of the program to execute.
The boundaries of the polytopes, the data dependencies, and the transformations are often described using systems of constraints, and this approach is often referred to as a constraint-based approach to loop optimization. For example, a single statement within an outer loop ' for i := 0 to n ' and an inner loop ' for j := 0 to i+2 ' is executed ...