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| C.T. King, W.H. Chou, L.M. Ni, "Pipelined Data Parallel Algorithms-I: Concept and Modeling," IEEE Transactions on Parallel and Distributed Systems, vol. 1, no. 4, pp. 470-485, October, 1990. | |||
| BibTex | x | ||
| @article{ 10.1109/71.80175, author = {C.T. King and W.H. Chou and L.M. Ni}, title = {Pipelined Data Parallel Algorithms-I: Concept and Modeling}, journal ={IEEE Transactions on Parallel and Distributed Systems}, volume = {1}, number = {4}, issn = {1045-9219}, year = {1990}, pages = {470-485}, doi = {http://doi.ieeecomputersociety.org/10.1109/71.80175}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - JOUR JO - IEEE Transactions on Parallel and Distributed Systems TI - Pipelined Data Parallel Algorithms-I: Concept and Modeling IS - 4 SN - 1045-9219 SP470 EP485 EPD - 470-485 A1 - C.T. King, A1 - W.H. Chou, A1 - L.M. Ni, PY - 1990 KW - Index Termspipelined data-parallel algorithms; pipelined operations; data level partitioning; data parallelism; Petri nets; parallel algorithms VL - 1 JA - IEEE Transactions on Parallel and Distributed Systems ER - | |||
The basic concept of pipelined data-parallel algorithms is introduced by contrasting the algorithms with other styles of computation and by a simple example (a pipeline image distance transformation algorithm). Pipelined data-parallel algorithms are a class of algorithms which use pipelined operations and data level partitioning to achieve parallelism. Applications which involve data parallelism and recurrence relations are good candidates for this kind of algorithm. The computations are ideal for distributed-memory multicomputers. By controlling the granularity through data partitioning and overlapping the operations through pipelining, it is possible to achieve a balanced computation on multicomputers. An analytic model is presented for modeling pipelined data-parallel computation on multicomputers. The model uses timed Petri nets to describe data pipelining operations. As a case study, the model is applied to a pipelined matrix multiplication algorithm. Predicted results match closely with the measured performance on a 64-node NCUBE hypercube multicomputer.
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