**Training Images-Based Stochastic Simulation on Many-Core Architectures**

Tao Huang and Detang Lu

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/64276

#### **Abstract**

In the past decades, multiple-point geostatistical methods (MPS) are increasing in popularity in various fields. Compared with the traditional techniques, MPS techni‐ ques have the ability to characterize geological reality that commonly has complex structures such as curvilinear and long-range channels by using high-order statistics for pattern reconstruction. As a result, the computational burden is heavy, and some‐ times, the current algorithms are unable to be applied to large-scale simulations. With the continuous development of hardware architectures, the parallelism implementa‐ tion of MPS methods is an alternative to improve the performance. In this chapter, we overview the basic elements for MPS methods and provide several parallel strategies on many-core architectures. The GPU-based parallel implementation of two efficient MPS methods known as SNESIM and Direct Sampling is detailed as examples.

**Keywords:** geostatistics, multiple point, stochastic simulation, training image, manycore architecture
