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|Title:||Supporting parallel computing on a distributed object architecture|
|Authors:||Wang, C. |
|Citation:||Wang, C., Teo, Y.M. (2001). Supporting parallel computing on a distributed object architecture. Journal of Systems and Software 56 (3) : 261-278. ScholarBank@NUS Repository. https://doi.org/10.1016/S0164-1212(00)00102-3|
|Abstract:||The availability of high-speed networks and increasingly powerful commodity microprocessors is making the usage of clusters, or networks, of computers an appealing platform for cost effective parallel computing. However, the ease of developing efficient high-performance parallel software to exploit these platforms presents a major challenge. Advances in distributed object software technology have made the management of distributed computing resources easier than before. This also brings many benefits for parallel computing. Firstly, distributed object technology facilitates the encapsulating of parallel computing resources into a uniform model despite their differences in implementations that are based on different languages executing on different platforms. Secondly, mature object-oriented analysis, design method, as well as component idea embodied in distributed object technology can enhance the reusability of parallel software. To support parallel computing in a distributed object-based computing platform, a uniform high performance distributed object architecture layer is necessary. In this paper, we propose a distributed object-based framework called DoHPC to support parallel computing on distributed object architectures. We present the use of dependence analysis technique to exploit intra-object parallelism and an interoperability model for supporting distributed parallel objects. Experimental results on a Fujitsu AP3000 workstation cluster consisting of a cluster of 32 UltraSPARC workstations show that the implementation of inter-object parallelism on a workstation cluster environment is efficient. With intra-object parallel computation speedup efficiency is greater than 90% and with overhead of less than 10% for large problem, and the interoperability model improves speedup by 20%.|
|Source Title:||Journal of Systems and Software|
|Appears in Collections:||Staff Publications|
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