157,183 research outputs found
Multi-Level Parallelism for Incompressible Flow Computations on GPU Clusters
We investigate multi-level parallelism on GPU clusters with MPI-CUDA and hybrid MPI-OpenMP-CUDA parallel implementations, in which all computations are done on the GPU using CUDA. We explore efficiency and scalability of incompressible flow computations using up to 256 GPUs on a problem with approximately 17.2 billion cells. Our work addresses some of the unique issues faced when merging fine-grain parallelism on the GPU using CUDA with coarse-grain parallelism that use either MPI or MPI-OpenMP for communications. We present three different strategies to overlap computations with communications, and systematically assess their impact on parallel performance on two different GPU clusters. Our results for strong and weak scaling analysis of incompressible flow computations demonstrate that GPU clusters offer significant benefits for large data sets, and a dual-level MPI-CUDA implementation with maximum overlapping of computation and communication provides substantial benefits in performance. We also find that our tri-level MPI-OpenMP-CUDA parallel implementation does not offer a significant advantage in performance over the dual-level implementation on GPU clusters with two GPUs per node, but on clusters with higher GPU counts per node or with different domain decomposition strategies a tri-level implementation may exhibit higher efficiency than a dual-level implementation and needs to be investigated further
Climbing depth-bounded adjacent discrepancy search for solving hybrid flow shop scheduling problems with multiprocessor tasks
This paper considers multiprocessor task scheduling in a multistage hybrid
flow-shop environment. The problem even in its simplest form is NP-hard in the
strong sense. The great deal of interest for this problem, besides its
theoretical complexity, is animated by needs of various manufacturing and
computing systems. We propose a new approach based on limited discrepancy
search to solve the problem. Our method is tested with reference to a proposed
lower bound as well as the best-known solutions in literature. Computational
results show that the developed approach is efficient in particular for
large-size problems
Large scale simulation of turbulence using a hybrid spectral/finite difference solver
Performing Direct Numerical Simulation (DNS) of turbulence on large-scale systems (offering more than 1024 cores) has become a challenge in high performance computing. The computer power increase allows now to solve flow problems on
large grids (with close to 10^9 nodes). Moreover these large scale simulations can be performed on non-homogeneous turbulent flows. A reasonable amount of time is needed to converge statistics if the large grid size is combined with a large number of cores. To this end we developed a Navier-Stokes solver, dedicated to situations where only one direction is heterogeneous, and particularly suitable for massive parallel architecture. Based on an hybrid approach spectral/finite-difference, we use a volumetric decomposition of the domain to extend the FFTs computation to a large number of cores. Scalability tests using up to 32K cores as well as preliminary results of a full simulation are presented
- …