Evaluation of release consistent software distributed shared memory on emerging network technology
ISCA '93 Proceedings of the 20th annual international symposium on computer architecture
Dynamic data prefetching in home-based software DSMs
Journal of Computer Science and Technology
Optimizing Home-Based Software DSM Protocols
Cluster Computing
Improving the performance of DSM systems via compiler involvement
Proceedings of the 1994 ACM/IEEE conference on Supercomputing
JIAJIA: A Software DSM System Based on a New Cache Coherence Protocol
HPCN Europe '99 Proceedings of the 7th International Conference on High-Performance Computing and Networking
Reducing System Overheads in Home-based Software DSMs
IPPS '99/SPDP '99 Proceedings of the 13th International Symposium on Parallel Processing and the 10th Symposium on Parallel and Distributed Processing
A Comparison of Two Strategies of Dynamic Data Prefetching in Software DSM
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
Write Detection in Home-Based Software DSMs
Euro-Par '99 Proceedings of the 5th International Euro-Par Conference on Parallel Processing
Evaluation of the JIAJIA Software DSM System on High Performance Computer Architectures
HICSS '99 Proceedings of the Thirty-second Annual Hawaii International Conference on System Sciences-Volume 8 - Volume 8
Adaptive Granularity: Transparent Integration of Fine- and Coarse-Grain Communications
PACT '96 Proceedings of the 1996 Conference on Parallel Architectures and Compilation Techniques
On Design of Agent Home Scheme for Prefetching Strategy in DSM Systems
AINA '05 Proceedings of the 19th International Conference on Advanced Information Networking and Applications - Volume 1
On the Design and Implementation of an Effective Prefetch Strategy for DSM Systems
The Journal of Supercomputing
Future Generation Computer Systems
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High speed networks and rapidly improving microprocessor performance make the network of workstations an extremely important tool for parallel computing in order to speedup the execution of scientific applications. Shared memory is an attractive programming model for designing parallel and distributed applications, where the programmer can focus on algorithmic development rather than data partition and communication. Based on this important characteristic, the design of systems to provide the shared memory abstraction on physically distributed memory machines has been developed, known as Distributed Shared Memory (DSM). DSM is built using specific software to combine a number of computer hardware resources into one computing environment. Such an environment not only provides an easy way to execute parallel applications, but also combines available computational resources with the purpose of speeding up execution of these applications. DSM systems need to maintain data consistency in memory, which usually leads to communication overhead. Therefore, there exists a number of strategies that can be used to overcome this overhead issue and improve overall performance. Strategies as prefetching have been proven to show great performance in DSM systems, since they can reduce data access communication latencies from remote nodes. On the other hand, these strategies also transfer unnecessary prefetching pages to remote nodes. In this research paper, we focus on the access pattern during execution of a parallel application, and then analyze the data type and behavior of parallel applications. We propose an adaptive data classification scheme to improve prefetching strategy with the goal to improve overall performance. Adaptive data classification scheme classifies data according to the accessing sequence of pages, so that the home node uses past history access patterns of remote nodes to decide whether it needs to transfer related pages to remote nodes. From experimental results, we can observe that our proposed method can increase the accuracy of data access in effective prefetch strategy by reducing the number of page faults and misprefetching. Experimental results using our proposed classification scheme show a performance improvement of about 9---25% over the same benchmark applications running on top of an original JIAJIA DSM system.