High-performance data mining with skeleton-based structured parallel programming
Parallel Computing - Parallel data-intensive algorithms and applications
Efficient Memory Page Replacement on Web Server Clusters
ICCS '02 Proceedings of the International Conference on Computational Science-Part III
A Proposal of High Performance Data Mining System
PARA '02 Proceedings of the 6th International Conference on Applied Parallel Computing Advanced Scientific Computing
Research works on cluster computing and storage area network
Proceedings of the 3rd International Conference on Ubiquitous Information Management and Communication
A scheme of interactive data mining support system in parallel and distributed environment
ISPA'03 Proceedings of the 2003 international conference on Parallel and distributed processing and applications
A practical way to extend shared memory support beyond a motherboard at low cost
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
A cost-effective heuristic to schedule local and remote memory in cluster computers
The Journal of Supercomputing
A new degree of freedom for memory allocation in clusters
Cluster Computing
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Personal computer/Workstation (PC/WS) clusters are promising candidates for future high performance computers, because of their good scalability and cost performance ratio. Data intensive applications, such as data mining and ad hoc query processing in databases, are considered very important for massively parallel processors, as well as conventional scientific calculations. Thus, investigating the feasibility of data intensive applications on a PC cluster is meaningful.Association rule mining, one of the best-known problems in data mining, differs from conventional scientific calculations in its usage of main memory. It allocates many small data areas in main memory, and the number of those areas suddenly grows enormously during execution. As a result, the contents of memory must be swapped out if the requirement for memory space exceeds the real memory size. However, because the size of each data area is rather small and the elements are accessed almost at random, swapping out to a storage device must degrade the performance severely.In this paper, we investigate the feasibility of using available remote nodes' memory as a swap area when application execution nodes need to swap out their real memory contents during the execution of parallel data mining on PC clusters. We report our experiments in which application execution nodes acquire extra memory dynamically from several available remote nodes through an ATM network. A method of remote memory utilization with remote update operations is proposed and evaluated. The experimental results on our PC cluster show that the proposed method is expected to be considerably better than using hard disks as a swapping device. The dynamic decision mechanism for remote memory availability and the migration operations are also evaluated.