Back-propagation learning algorithm and parallel computers: The CLEPSYDRA mapping scheme
This paper deals with the parallel implementation of the back-propagation of errors learning algorithm. To obtain the partitioning of the neural network on the processor network the author describes a new mapping scheme that uses a mixture of synapse parallelism, neuron parallelism and training exam...
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| Veröffentlicht in: | Neurocomputing (Amsterdam) Jg. 31; H. 1; S. 67 - 85 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier B.V
01.03.2000
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| ISSN: | 0925-2312, 1872-8286 |
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| Abstract | This paper deals with the parallel implementation of the back-propagation of errors learning algorithm. To obtain the partitioning of the neural network on the processor network the author describes a new mapping scheme that uses a mixture of synapse parallelism, neuron parallelism and training examples parallelism (if any). The proposed mapping scheme allows to describe the back-propagation algorithm as a collection of SIMD processes, so that both SIMD and MIMD machines can be used. The main feature of the obtained parallel algorithm is the absence of point-to-point communication; in fact, for each training pattern, an all-to-one broadcasting with an associative operator (combination) and an one-to-all broadcasting (that can be both realized in log
P time) are needed. A performance model is proposed and tested on a ring-connected MIMD parallel computer. Simulation results on MIMD and SIMD parallel machines are also shown and commented. |
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| AbstractList | This paper deals with the parallel implementation of the back-propagation of errors learning algorithm. To obtain the partitioning of the neural network on the processor network the author describes a new mapping scheme that uses a mixture of synapse parallelism, neuron parallelism and training examples parallelism (if any). The proposed mapping scheme allows to describe the back-propagation algorithm as a collection of SIMD processes, so that both SIMD and MIMD machines can be used. The main feature of the obtained parallel algorithm is the absence of point-to-point communication; in fact, for each training pattern, an all-to-one broadcasting with an associative operator (combination) and an one-to-all broadcasting (that can be both realized in log P time) are needed. A performance model is proposed and tested on a ring-connected MIMD parallel computer. Simulation results on MIMD and SIMD parallel machines are also shown and commented. This paper deals with the parallel implementation of the back-propagation of errors learning algorithm. To obtain the partitioning of the neural network on the processor network the author describes a new mapping scheme that uses a mixture of synapse parallelism, neuron parallelism and training examples parallelism (if any). The proposed mapping scheme allows to describe the back-propagation algorithm as a collection of SIMD processes, so that both SIMD and MIMD machines can be used. The main feature of the obtained parallel algorithm is the absence of point-to-point communication; in fact, for each training pattern, an all-to-one broadcasting with an associative operator (combination) and an one-to-all broadcasting (that can be both realized in log P time) are needed. A performance model is proposed and tested on a ring-connected MIMD parallel computer. Simulation results on MIMD and SIMD parallel machines are also shown and commented. |
| Author | d'Acierno, Antonio |
| Author_xml | – sequence: 1 givenname: Antonio surname: d'Acierno fullname: d'Acierno, Antonio email: dacierno.a@irsip.na.cnr.it organization: I.R.S.I.P.-C.N.R. Via P. Castellino, 111-80131 Napoli-Italy |
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| CitedBy_id | crossref_primary_10_3745_KIPSTB_2004_11B_6_735 crossref_primary_10_1016_j_neucom_2016_06_014 crossref_primary_10_1016_j_neucom_2010_03_021 crossref_primary_10_1007_s13369_017_2907_2 |
| Cites_doi | 10.1016/0167-8191(87)90060-3 10.1016/0925-2312(92)90043-O 10.1016/0743-7315(92)90068-X 10.1109/71.313123 10.1109/72.165590 10.7551/mitpress/5236.001.0001 10.1109/EMPDP.1995.389157 10.1016/0167-8191(90)90084-M 10.1016/0167-8191(90)90083-L 10.1016/0743-7315(89)90065-8 10.1109/71.577255 10.1038/323533a0 10.1109/ICNN.1988.23925 10.1109/ICNN.1988.23922 10.1016/0167-8191(90)90085-N |
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| Keywords | MIMD parallel computers SIMD parallel computers Mapping scheme Back-propagation |
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Systems doi: 10.1109/71.577255 – volume: 323 start-page: 533 year: 1986 ident: 10.1016/S0925-2312(99)00151-4_BIB15 article-title: Learning representation by back-propagation of errors publication-title: Nature doi: 10.1038/323533a0 – ident: 10.1016/S0925-2312(99)00151-4_BIB10 doi: 10.1109/ICNN.1988.23925 – ident: 10.1016/S0925-2312(99)00151-4_BIB14 doi: 10.1109/ICNN.1988.23922 – ident: 10.1016/S0925-2312(99)00151-4_BIB3 – ident: 10.1016/S0925-2312(99)00151-4_BIB4 – volume: 14 start-page: 329 year: 1990 ident: 10.1016/S0925-2312(99)00151-4_BIB18 article-title: An implementation of back-propagation learning on GF11, a large SIMD parallel computer publication-title: Parallel Comput. doi: 10.1016/0167-8191(90)90085-N – ident: 10.1016/S0925-2312(99)00151-4_BIB13 |
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| SubjectTerms | Back-propagation Mapping scheme MIMD parallel computers SIMD parallel computers |
| Title | Back-propagation learning algorithm and parallel computers: The CLEPSYDRA mapping scheme |
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