The Experts below are selected from a list of 22626 Experts worldwide ranked by ideXlab platform
Maciej Paszynski - One of the best experts on this subject based on the ideXlab platform.
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hypergraph grammar based multi thread multi frontal direct solver with galois scheduler
Computer Science, 2019Co-Authors: Maciej Paszynski, Konrad Jopek, Anna Paszynska, Muhammad Amber Hasaan, Keshav PingaliAbstract:In this paper we analyze two dimensional grids with point and edge singularities in order to develop an efficient graph grammar based multi-frontal direct solver algorithm. We express these grids by hypergraph models. For these meshes we define a sequence of graph grammar productions expressing the construction of frontal matrices, elimination of fully assembled nodes, merging of resulting Schur complements, and repeating the process of elimination and merging until a single frontal matrix remains. The Dependency Relation between graph grammar productions is analyzed, and the Dependency graph is plot, which is equivalent to the elimination tree of the multi-frontal solver algorithm. We utilize classical multi-frontal solver algorithm, and the graph grammar productions allows us to construct an efficient elimination tree, based on the graph representation of the computational mesh, and not the global matrix itself. The graph grammar productions are assigned to nodes of the Dependency graph, and they are implemented as tasks in the GALOIS system and scheduled according to the developed Dependency graph over the shared memory parallel machine. We show that our graph grammar based solver outperforms parallel MUMPS solver.
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graph grammar based multi thread multi frontal parallel solver with trace theory based scheduler
International Conference on Conceptual Structures, 2010Co-Authors: Pawel Obrok, Pawel Pierzchala, Arkadiusz Szymczak, Maciej PaszynskiAbstract:The paper presents the graph grammar based multi-thread multi-frontal parallel direct solver for one and two dimensional Finite Difference Method (FDM). The multi-frontal solver algorithm has been expressed by graph grammar productions. Each production represents an atomic task that internally must be executed in serial. The sequence of graph grammar productions modeling the execution of the solver has been associated with the alphabet for the trace theory analysis. The Dependency Relation between tasks has been introduced based on the analysis of the solver algorithm. The sequence of productions has been transformed into the Foata Normal Form (FNF). The parallel solver algorithm has been implemented and tested on NVIDIA Cuda multi-core graphic card. The tasks have been scheduled according to the classes in the FNF.
Guodong Zhou - One of the best experts on this subject based on the ideXlab platform.
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semantic role labeling of chinese nominal predicates with Dependency driven constituent parse tree structure
Journal of Computer Science and Technology, 2013Co-Authors: Hongling Wang, Guodong ZhouAbstract:This paper explores a tree kernel based method for semantic role labeling (SRL) of Chinese nominal predicates via a convolution tree kernel. In particular, a new parse tree representation structure, called Dependency-driven constituent parse tree (D-CPT), is proposed to combine the advantages of both constituent and dependence parse trees. This is achieved by directly representing various kinds of Dependency Relations in a CPT-style structure, which employs Dependency Relation types instead of phrase labels in CPT (Constituent Parse Tree). In this way, D-CPT not only keeps the Dependency Relationship information in the Dependency parse tree (DPT) structure but also retains the basic hierarchical structure of CPT style. Moreover, several schemes are designed to extract various kinds of necessary information, such as the shortest path between the nominal predicate and the argument candidate, the support verb of the nominal predicate and the head argument modifled by the argument candidate, from D-CPT. This largely reduces the noisy information inherent in D-CPT. Finally, a convolution tree kernel is employed to compute the similarity between two parse trees. Besides, we also implement a feature-based method based on D-CPT. Evaluation on Chinese NomBank corpus shows that our tree kernel based method on D-CPT performs signiflcantly better than other tree kernel-based ones and achieves comparable performance with the state-of-the-art feature-based ones. This indicates the efiectiveness of the novel D-CPT structure in representing various kinds of Dependency Relations in a CPT-style structure and our tree kernel based method in exploring the novel D-CPT structure. This also illustrates that the kernel-based methods are competitive and they are complementary with the feature- based methods on SRL.
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chinese semantic role labeling with Dependency driven constituent parse tree structure
International conference natural language processing, 2012Co-Authors: Hongling Wang, Bukang Wang, Guodong ZhouAbstract:This paper explores a tree kernel-based method for nominal semantic role labeling (SRL). In particular, a new Dependency-driven constituent parse tree (D-CPT) structure is proposed to better represent the Dependency Relations in a CPT-style structure, which employs Dependency Relation types instead of phrase labels in CPT. In this way, D-CPT not only keeps the Dependency Relationship information in the Dependency parse tree (DPT) structure but also retains the basic structure of CPT. Moreover, several schemes are designed to extract various kinds of necessary information, such as the shortest path between the nominal predicate and the argument candidate, the support verb of the nominal predicate and the head argument modified by the argument candidate, from D-CPT . Evaluation on Chinese NomBank shows that our tree kernel-based method on D-CPT achieves comparable performance with the state-of-art feature-based ones. This indicates the effectiveness of the novel D-CPT structure for better representation of Dependency Relations in tree kernel-based methods. To our knowledge, this is the first research of tree kernel-based SRL on effectively exploring Dependency Relationship information, which achieves comparable performance with the state-of-the-art feature-based ones.
Hongling Wang - One of the best experts on this subject based on the ideXlab platform.
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semantic role labeling of chinese nominal predicates with Dependency driven constituent parse tree structure
Journal of Computer Science and Technology, 2013Co-Authors: Hongling Wang, Guodong ZhouAbstract:This paper explores a tree kernel based method for semantic role labeling (SRL) of Chinese nominal predicates via a convolution tree kernel. In particular, a new parse tree representation structure, called Dependency-driven constituent parse tree (D-CPT), is proposed to combine the advantages of both constituent and dependence parse trees. This is achieved by directly representing various kinds of Dependency Relations in a CPT-style structure, which employs Dependency Relation types instead of phrase labels in CPT (Constituent Parse Tree). In this way, D-CPT not only keeps the Dependency Relationship information in the Dependency parse tree (DPT) structure but also retains the basic hierarchical structure of CPT style. Moreover, several schemes are designed to extract various kinds of necessary information, such as the shortest path between the nominal predicate and the argument candidate, the support verb of the nominal predicate and the head argument modifled by the argument candidate, from D-CPT. This largely reduces the noisy information inherent in D-CPT. Finally, a convolution tree kernel is employed to compute the similarity between two parse trees. Besides, we also implement a feature-based method based on D-CPT. Evaluation on Chinese NomBank corpus shows that our tree kernel based method on D-CPT performs signiflcantly better than other tree kernel-based ones and achieves comparable performance with the state-of-the-art feature-based ones. This indicates the efiectiveness of the novel D-CPT structure in representing various kinds of Dependency Relations in a CPT-style structure and our tree kernel based method in exploring the novel D-CPT structure. This also illustrates that the kernel-based methods are competitive and they are complementary with the feature- based methods on SRL.
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chinese semantic role labeling with Dependency driven constituent parse tree structure
International conference natural language processing, 2012Co-Authors: Hongling Wang, Bukang Wang, Guodong ZhouAbstract:This paper explores a tree kernel-based method for nominal semantic role labeling (SRL). In particular, a new Dependency-driven constituent parse tree (D-CPT) structure is proposed to better represent the Dependency Relations in a CPT-style structure, which employs Dependency Relation types instead of phrase labels in CPT. In this way, D-CPT not only keeps the Dependency Relationship information in the Dependency parse tree (DPT) structure but also retains the basic structure of CPT. Moreover, several schemes are designed to extract various kinds of necessary information, such as the shortest path between the nominal predicate and the argument candidate, the support verb of the nominal predicate and the head argument modified by the argument candidate, from D-CPT . Evaluation on Chinese NomBank shows that our tree kernel-based method on D-CPT achieves comparable performance with the state-of-art feature-based ones. This indicates the effectiveness of the novel D-CPT structure for better representation of Dependency Relations in tree kernel-based methods. To our knowledge, this is the first research of tree kernel-based SRL on effectively exploring Dependency Relationship information, which achieves comparable performance with the state-of-the-art feature-based ones.
Keshav Pingali - One of the best experts on this subject based on the ideXlab platform.
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hypergraph grammar based multi thread multi frontal direct solver with galois scheduler
Computer Science, 2019Co-Authors: Maciej Paszynski, Konrad Jopek, Anna Paszynska, Muhammad Amber Hasaan, Keshav PingaliAbstract:In this paper we analyze two dimensional grids with point and edge singularities in order to develop an efficient graph grammar based multi-frontal direct solver algorithm. We express these grids by hypergraph models. For these meshes we define a sequence of graph grammar productions expressing the construction of frontal matrices, elimination of fully assembled nodes, merging of resulting Schur complements, and repeating the process of elimination and merging until a single frontal matrix remains. The Dependency Relation between graph grammar productions is analyzed, and the Dependency graph is plot, which is equivalent to the elimination tree of the multi-frontal solver algorithm. We utilize classical multi-frontal solver algorithm, and the graph grammar productions allows us to construct an efficient elimination tree, based on the graph representation of the computational mesh, and not the global matrix itself. The graph grammar productions are assigned to nodes of the Dependency graph, and they are implemented as tasks in the GALOIS system and scheduled according to the developed Dependency graph over the shared memory parallel machine. We show that our graph grammar based solver outperforms parallel MUMPS solver.
Pawel Obrok - One of the best experts on this subject based on the ideXlab platform.
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graph grammar based multi thread multi frontal parallel solver with trace theory based scheduler
International Conference on Conceptual Structures, 2010Co-Authors: Pawel Obrok, Pawel Pierzchala, Arkadiusz Szymczak, Maciej PaszynskiAbstract:The paper presents the graph grammar based multi-thread multi-frontal parallel direct solver for one and two dimensional Finite Difference Method (FDM). The multi-frontal solver algorithm has been expressed by graph grammar productions. Each production represents an atomic task that internally must be executed in serial. The sequence of graph grammar productions modeling the execution of the solver has been associated with the alphabet for the trace theory analysis. The Dependency Relation between tasks has been introduced based on the analysis of the solver algorithm. The sequence of productions has been transformed into the Foata Normal Form (FNF). The parallel solver algorithm has been implemented and tested on NVIDIA Cuda multi-core graphic card. The tasks have been scheduled according to the classes in the FNF.