Zobrazeno 1 - 10
of 99
pro vyhledávání: '"Ng, Esmond G."'
The solution of sparse symmetric positive definite linear systems is an important computational kernel in large-scale scientific and engineering modeling and simulation. We will solve the linear systems using a direct method, in which a Cholesky fact
Externí odkaz:
http://arxiv.org/abs/2409.14009
We present a new variant of serial right-looking supernodal sparse Cholesky factorization (RL). Our comparison of RL with the multifrontal method confirms that RL is simpler, slightly faster, and requires slightly less storage. The key to the rest of
Externí odkaz:
http://arxiv.org/abs/2409.13090
We present a graph bisection and partitioning algorithm based on graph neural networks. For each node in the graph, the network outputs probabilities for each of the partitions. The graph neural network consists of two modules: an embedding phase and
Externí odkaz:
http://arxiv.org/abs/2110.08614
Graph Partitioning and Sparse Matrix Ordering using Reinforcement Learning and Graph Neural Networks
We present a novel method for graph partitioning, based on reinforcement learning and graph convolutional neural networks. Our approach is to recursively partition coarser representations of a given graph. The neural network is implemented using SAGE
Externí odkaz:
http://arxiv.org/abs/2104.03546
Autor:
Negoita, Gianina Alina, Vary, James P., Luecke, Glenn R., Maris, Pieter, Shirokov, Andrey M., Shin, Ik Jae, Kim, Youngman, Ng, Esmond G., Yang, Chao, Lockner, Matthew, Prabhu, Gurpur M.
Publikováno v:
Phys. Rev. C 99, 054308 (2019)
Ab initio approaches in nuclear theory, such as the no-core shell model (NCSM), have been developed for approximately solving finite nuclei with realistic strong interactions. The NCSM and other approaches require an extrapolation of the results obta
Externí odkaz:
http://arxiv.org/abs/1810.04009
Autor:
Negoita, Gianina Alina, Luecke, Glenn R., Vary, James P., Maris, Pieter, Shirokov, Andrey M., Shin, Ik Jae, Kim, Youngman, Ng, Esmond G., Yang, Chao
Publikováno v:
Proceedings of the Ninth International Conference on Computational Logics, Algebras, Programming, Tools, and Benchmarking COMPUTATION TOOLS 2018 February 18-22, 2018, Barcelona, Spain
In recent years, several successful applications of the Artificial Neural Networks (ANNs) have emerged in nuclear physics and high-energy physics, as well as in biology, chemistry, meteorology, and other fields of science. A major goal of nuclear the
Externí odkaz:
http://arxiv.org/abs/1803.03215
Autor:
Van Beeumen, Roel, Williams-Young, David B., Kasper, Joseph M., Yang, Chao, Ng, Esmond G., Li, Xiaosong
The ab initio description of the spectral interior of the absorption spectrum poses both a theoretical and computational challenge for modern electronic structure theory. Due to the often spectrally dense character of this domain in the quantum propa
Externí odkaz:
http://arxiv.org/abs/1704.05923
Ordering vertices of a graph is key to minimize fill-in and data structure size in sparse direct solvers, maximize locality in iterative solvers, and improve performance in graph algorithms. Except for naturally parallelizable ordering methods such a
Externí odkaz:
http://arxiv.org/abs/1610.08128
Publikováno v:
Computer Physics Communications, 222:1--13, 2018
We describe a number of recently developed techniques for improving the performance of large-scale nuclear configuration interaction calculations on high performance parallel computers. We show the benefit of using a preconditioned block iterative me
Externí odkaz:
http://arxiv.org/abs/1609.01689
Autor:
Bogner, Scott, Bulgac, Aurel, Carlson, Joseph A., Engel, Jonathan, Fann, George, Furnstahl, Richard J., Gandolfi, Stefano, Hagen, Gaute, Horoi, Mihai, Johnson, Calvin W., Kortelainen, Markus, Lusk, Ewing, Maris, Pieter, Nam, Hai Ah, Navratil, Petr, Nazarewicz, Witold, Ng, Esmond G., Nobre, Gustavo P. A., Ormand, Erich, Papenbrock, Thomas, Pei, Junchen, Pieper, Steven C., Quaglioni, Sofia, Roche, Kenneth J., Sarich, Jason, Schunck, Nicolas, Sosonkina, Masha, Terasaki, Jun, Thompson, Ian J., Vary, James P., Wild, Stefan M.
The UNEDF project was a large-scale collaborative effort that applied high-performance computing to the nuclear quantum many-body problem. UNEDF demonstrated that close associations among nuclear physicists, mathematicians, and computer scientists ca
Externí odkaz:
http://arxiv.org/abs/1304.3713