| With the rapid development of high-performance parallel computing technology, it ismore and more important for geologists to use the new multicore and GPU computingplatform to compute physical and chemical state under complicated geological conditions bynumerical simulation. General Purpose GPU computing, in combination with the undergroundmultiphase flow numerical simulation, may be effective tool for variety of hydrogeology andenvironmental geology problems. TOUGHREACT, developed by Lawrence BerkeleyLaboratory,is currently the most widely used simulation program of underground multiphasefluid motion and transport of chemical reaction process and mechanism of the Earth Simulator.At present, when involving large scale and high precision, and high complexity of large scalenumerical simulation problems (such as nuclear waste underground disposal, such asunderground storage of CO2), TOUGHREACT is ineffective. Therefore, using the GPUparallel computing technology to accumulate the numerical simulation of TOUGHREACT,has very important engineering significance and research value.For this purpose, this articleworks on numerical simulation software parallelization based on CPU-GPU.First of all, to briefly understand the basic simulation process of the program, I study theknowledge of the relevant expertise. Reference to the existing research work, carried out adetailed analysis of the modular structure of the software. According to the comparison ofmultiphase flow module and geochemical reactions migration module differences, I foundthat the numerical simulation of multiphase flow part are more suitable in parallel on a GPUplatform because of the solution process in terms of the size of linear equations and theconcurrency of the iterative process.When solving partial differential equations, sparse linear equations have a very importantrole. Often use partial differential equations as mathematical models. Then, based on thecomparison of its various parts of the module execution time, I decide to parallel the solver oflinear equations which account for more than80%of the simulation time.Since the coefficient matrix for solving multiphase flow problems encountered withnon-symmetric non-positive definite characteristics, the article uses several pairs of conjugategradient method for solving equations in the Krylov subspace methods. Analysis of thepreconditioned conjugate gradient method using in the solver, is done. In order not to come atthe expense of solving efficiency, I decided not to use GPU accelerating pretreatment section.CUDA implementation focused on the sparse matrix-vector multiplication (SPMV) and vectorinner which are two of the most time consuming parts of the solution. CUDA-based parallelprogram development is not difficult. The main task is how to optimize it. This paper made a lot of work on it. Including the selection of a sparse matrix storage format; reduce the hostand client data traffic; take a basic matrix-vector multiplication algorithm is divided into twomethods, so the computing will be more efficient; optimize the organizational structure ofkernel for reducing the core switching overhead; use shared memory and the page-lockedmemory to optimize memory access; design multiple versions of each operating kernel threadorganization; set up the thread scale tree, so that the size of the problem; make full use ofprocessor resources on the GPU. Availability of the program has increased greatly.Finally, the parallel preconditioned conjugate gradient solver package is integrated toTOUGHREACT programs. In order to test the performance, we use the parallel BICG andparallel BICGSTB algorithm to simulate the practical problems of different sizes on theCPU-GPU computing platform. The experiments show that it can speed up the solvingprocess of the linear equations3.4times in double precision. Through the parallelization ofthe mainly part of the program, overall simulation of the solution process also has been wellaccelerated and can speed up the total solving process of the simulation2.8times. This resultconfirms the correctness of the parallelization strategy used in this article and laid a goodfoundation for further parallelization of geochemical reactions migration module on GPU andhas accumulated rich experience. |
