Multi-core strategies for particle methods

This paper discusses the implementation of particle based numerical methods on multi-core machines. In contrast to cluster computing, where memory is distributed across machines, multi-core machine can share memory across all cores. Here general strategies are developed for spatial management of particles and sub-domains that optimize computation on shared memory machines. In particular, we extend cell hashing so that cells bundle particles into orthogonal tasks that can be safely distributed across cores avoiding the use of "memory locks" while still protecting against race conditions. Adjusting task size provides for optimal load balancing and maximizing cache hits. Additionally, the way in which tasks are mapped to execution threads has a significant influence on the memory footprint and it is shown that minimizing memory usage is one of the most important factors in achieving execution speed and performance on multi-core. A novel algorithm called H-Dispatch is used to pipeline tasks to processing cores. The performance is demonstrated in speed-up and efficiency tests on a smooth particle hydrodynamics (SPH) flow simulator. An efficiency of over 90% is achieved on a 24-core machine

A Multi-Core Numerical Framework for Characterizing Flow in Oil Reservoirs

Presented at the SCS Spring Simulation Multi-Conference – SpringSim 2011, April 4-7, 2011 – Boston, USA Awarded Best Paper in the 19th High Performance Computing Symposium and Best Overall Paper at SpringSim 2011.This paper presents a numerical framework that enables scalable, parallel execution of engineering simulations on multi-core, shared memory architectures. Distribution of the simulations is done by selective hash-tabling of the model domain which spatially decomposes it into a number of orthogonal computational tasks. These tasks, the size of which is critical to optimal cache blocking and consequently performance, are then distributed for execution to multiple threads using the previously presented task management algorithm, H-Dispatch. Two numerical methods, smoothed particle hydrodynamics (SPH) and the lattice Boltzmann method (LBM), are discussed in the present work, although the framework is general enough to be used with any explicit time integration scheme. The implementation of both SPH and the LBM within the parallel framework is outlined, and the performance of each is presented in terms of speed-up and efficiency. On the 24-core server used in this research, near linear scalability was achieved for both numerical methods with utilization efficiencies up to 95%. To close, the framework is employed to simulate fluid flow in a porous rock specimen, which is of broad geophysical significance, particularly in enhanced oil recovery

Characterizing Flow in Oil Reservoir Rock Using Smooth Particle Hydrodynamics

In this paper, a 3D Smooth Particle Hydrodynamics (SPH) simulator for modeling grain scale fluid flow in porous rock is presented. The versatility of the SPH method has driven its use in increasingly complex areas of flow analysis, including flows related to porous rock for both groundwater and petroleum reservoir research. Previous approaches to such problems using SPH have involved the use of idealized pore geometries (cylinder/sphere packs etc.). In this paper we discuss the characterization of flow in models with geometries acquired from 3D X-ray microtomograph images of actual porous rock. One key advantage of SPH is realized when considering the complexity of multiple fluid phases (e.g., water and oil). By incorporating interfacial physics such as surface tension and wettability, it is possible to model the capillary behavior of multiple fluid phases with accuracy. Simulation results for permeability will be presented and compared to those from experimentation and other numerical methods showing good agreement and validating the method. By accurately reproducing the flow characteristics of actual porous rock samples, this work has made significant progress towards validating SPH for such applications.Saudi AramcoSchlumberger-Doll Research Cente

A multicore numerical framework for assessing the permeability of reservoir rocks

This paper presents a numerical framework that is capable of simulating multiphase flow in reservoir rocks at the pore scale. The framework combines a suite of numerical methods, including smooth particle hydrodynamics (SPH) and the lattice Boltzmann method (LBM), with shared-memory, multicore parallel processing to increase the flexibility and scalability of solutions. By incorporating a suite of methods in the numerical framework, each with their own relative strengths, the range of problems that can be solved is greatly increased. The utilized parallel programming model exploits the large memory as well as the low latency of processor caches available in contemporary multicore servers. Maximized cache performance is achieved by taking a fine-grained approach to domain decomposition and also taking advantage of the spatial locality of data in the solvers. This results in scalable speed-up efficiency, whilst the asynchronous distribution of fine-grained, parallel work tasks results in natural load balancing. Both the SPH and LBM solvers are applied to determine the permeability of reservoir rocks from x-ray microtomographic images of samples. Predictions of the absolute permeability of West Texas Dolomite and Berea Sandstone samples are presented, with both comparing well with experimental data