Artículo de revista
Tuning and hybrid parallelization of a genetic-based multi-point statistics simulation code
Fecha
2014Registro en:
Parallel Computing 40 (2014) 144–158
dx.doi.org/10.1016/j.parco.2014.04.005
Autor
Peredo Andrade, Oscar Francisco
Ortiz Cabrera, Julián
Herrero, José R.
Samaniego, Cristóbal
Institución
Resumen
One of the main difficulties using multi-point statistical (MPS) simulation based on annealing
techniques or genetic algorithms concerns the excessive amount of time and memory
that must be spent in order to achieve convergence. In this work we propose code
optimizations and parallelization schemes over a genetic-based MPS code with the aim
of speeding up the execution time. The code optimizations involve the reduction of cache
misses in the array accesses, avoid branching instructions and increase the locality of the
accessed data. The hybrid parallelization scheme involves a fine-grain parallelization of
loops using a shared-memory programming model (OpenMP) and a coarse-grain
distribution of load among several computational nodes using a distributed-memory programming
model (MPI). Convergence, execution time and speed-up results are presented
using 2D training images of sizes 100 100 1 and 1000 1000 1 on a distributedshared
memory supercomputing facility.