Introduction to OpenMP (Originally for CS 838, Fall 2005)课件.pptVIP

Microbenchmark: Ocean for( t=0; t t_steps; t++) { for( x=0; x x_dim; x++) { for( y=0; y y_dim; y++) { ocean[x][y] = /* avg of neighbors */ } } } #pragma omp parallel for \ shared(ocean,x_dim,y_dim) private(x,y) // Implicit Barrier Synchronization temp_ocean = ocean; ocean = other_ocean; other_ocean = temp_ocean; 精品文档 Microbenchmark: Ocean ocean_dynamic – Traverses entire ocean, row-by-row, assigning row iterations to threads with dynamic scheduling. ocean_static – Traverses entire ocean, row-by-row, assigning row iterations to threads with static scheduling. ocean_squares – Each thread traverses a square-shaped section of the ocean. Loop-level scheduling not used—loop bounds for each thread are determined explicitly. ocean_pthreads – Each thread traverses a square-shaped section of the ocean. Loop bounds for each thread are determined explicitly. OpenMP PThreads 精品文档 Microbenchmark: Ocean 精品文档 Microbenchmark: Ocean 精品文档 Microbenchmark: GeneticTSP Genetic heuristic-search algorithm for approximating a solution to the traveling salesperson problem Operates on a population of possible TSP paths Forms new paths by combining known, good paths (crossover) Occasionally introduces new random elements (mutation) Variables: Np – Population size, determines search space and working set size Ng – Number of generations, controls effort spent refining solutions rC – Rate of crossover, determines how many new solutions are produced and evaluated in a generation rM – Rate of mutation, determines how often new (random) solutions are introduced 精品文档 Microbenchmark: GeneticTSP while( current_gen Ng ) { Breed rC*Np new solutions: Select two parents Perform crossover() Mutate() with probability rM Evaluate() new solution Identify least-fit rC*Np solutions: Remove unfit solutions from population current_gen++ } return the most fit solution found Outer loop has data dependence between iterations, as the population is no