Improving resilience of scientific software through a domain-specific approach

Autor: Michael B. Giles, Satheesh Maheswaran, Gihan R. Mudalige, Istvan Z. Reguly

Publikováno v: Journal of Parallel and Distributed Computing. 128:99-114

In this paper we present research on improving the resilience of the execution of scientific software, an increasingly important concern in High Performance Computing (HPC). We build on an existing high-level abstraction framework, the Oxford Paralle

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::94e6a679a3b49a5c5734a72f7867799f
https://doi.org/10.1016/j.jpdc.2019.01.015

Multilevel and Quasi Monte Carlo methods for the calculation of the Expected Value of Partial Perfect Information

Autor: Zhenru Wang, Nicky J Welton, Howard Thom, Christopher Jackson, Christophe Andrieu, Wei Fang, Michael B. Giles

Publikováno v: Fang, W, Wang, Z, Giles, M B, Jackson, C H, Welton, N J, Andrieu, C & Thom, H 2022, ' Multilevel and Quasi Monte Carlo Methods for the Calculation of the Expected Value of Partial Perfect Information ', Medical Decision Making, vol. 42, no. 2, pp. 168-181 . https://doi.org/10.1177/0272989X211026305
Medical Decision Making

The expected value of partial perfect information (EVPPI) provides an upper bound on the value of collecting further evidence on a set of inputs to a cost-effectiveness decision model. Standard Monte Carlo (MC) estimation of EVPPI is computationally

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e6c8cee9a54e2aa937eb9d772b2c3842
https://doi.org/10.1101/2021.03.30.21254626

QMC Sampling from Empirical Datasets

Autor: Fei Xie, Zhijian He, Michael B. Giles

Publikováno v: Springer Proceedings in Mathematics & Statistics ISBN: 9783030434649

This paper presents a simple idea for the use of quasi-Monte Carlo sampling with empirical datasets, such as those generated by MCMC methods. It also presents and analyses a related idea of taking advantage of the Hilbert space-filling curve. Theoret

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6bf03e78a80ca2400feffcf39a70ecf9
https://doi.org/10.1007/978-3-030-43465-6_26

Multilevel Estimation of Expected Exit Times and Other Functionals of Stopped Diffusions

Autor: Michael B. Giles, Francisco Bernal

Publikováno v: e-Archivo: Repositorio Institucional de la Universidad Carlos III de Madrid
Universidad Carlos III de Madrid (UC3M)
e-Archivo. Repositorio Institucional de la Universidad Carlos III de Madrid
instname

This paper proposes and analyses a new multilevel Monte Carlo method for the estimation of mean exit times for multi-dimensional Brownian diffusions, and associated functionals which correspond to solutions to high-dimensional parabolic PDEs through

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::849c86012121279916680cd27d38fe8e
https://doi.org/10.1137/17m1116660

Combining sparse grids, multilevel MC and QMC for elliptic PDEs with random coefficients

Autor: Frances Y. Kuo, Ian H. Sloan, Michael B. Giles

Publikováno v: Springer Proceedings in Mathematics & Statistics ISBN: 9783319914350

Building on previous research which generalized multilevel Monte Carlo methods using either sparse grids or Quasi-Monte Carlo methods, this paper considers the combination of all these ideas applied to elliptic PDEs with finite-dimensional uncertaint

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e0be6958713c92a78636a2461550806e
https://ora.ox.ac.uk/objects/uuid:9a30a9a7-9747-4616-aa78-134d9c8416b3