R-tree is a very popular dynamic access structure cable of storing multidimensional and spatial data . Considering it's merit of the efficient global balance and dynamic reorganization ,We try to use R-tree to decluster the multiattribute data in database system or file system . As Many previous multiattribute declustering mechanisms do not take into account the properties of the Cluster of Workstations (COW) , we present the Global Parallel R-Tree (GPR-Tree) under the architecture of COW. Firstly we inspect the issues in efficiency of R-tree and it's variants , we try to enhance the R-Tree efficiency by using heuristics information in the reconstruction of R-Tree during the node splitting and the treatment of the orphan entries of the underfilled node. Then we parallelize the improved R-Tree among the components in the system. The basic idea is to alleviate the bottleneck effect of the I/O subsystem, making use of the high speed network communication and the memory. The GPR-Tree is shared among the processing units (PU) of the system . We use a mixed LRU algorithm to schedule pages in memory to maintain the nodes visited frequently in memory . A write-update-like protocol is used to keep the coherency among multiple copies maintained in the system . This mechanism will be proved efficient to improve the scalability and performance of the system.