In recent years

with the rapid growth of scientific data

large data analysis has become urgent problems. More and more large-data processing methods are modified to perform computation under parallel and distributed computing environment.In this paper

we present a hybrid architecture for large volume data visualization and processing.Various hardware environments and technologies are integrated in this architecture to perform interactive operations on very large volume datasets.All the datasets are stored in a data center with a gigabit network environment.The time-consuming data processing tasks are dispatched to the computing nodes connected to the same network

while the visualization and interaction operations are executed on a high-performance graphics workstation.OpenCL and OpenMP are used to implement volume rendering algorithms for accelerating visualization of a hierarchical volume data structure by both GPU and CPU with multi-cores

and some out-of-core algorithms are also presented to process the large dataset directly.The experimental results and practical application indicate that the hybrid architecture and methods presented in this paper are effective and efficient for the processing and visualization of very large volume datasets.