FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4308451471> ?p ?o ?g. }

• W4308451471 abstract "Abstract Parallel computing in exact Gaussian process (GP) calculations becomes necessary for avoiding computational and memory restrictions associated with large‐scale environmental data science applications. The exact evaluation of the Gaussian log‐likelihood function requires storage and operations, where is the number of geographical locations. Thus, exactly computing the log‐likelihood function with a large number of locations requires exploiting the power of existing parallel computing hardware systems, such as shared‐memory, possibly equipped with GPUs, and distributed‐memory systems, to solve this exact computational complexity. In this article, we present ExaGeoStatR , a package for exascale geostatistics in R that supports a parallel computation of the exact maximum likelihood function on a wide variety of parallel architectures. Furthermore, the package allows scaling existing GP methods to a large spatial/temporal domain. Prohibitive exact solutions for large geostatistical problems become possible with ExaGeoStatR . Parallelization in ExaGeoStatR depends on breaking down the numerical linear algebra operations in the log‐likelihood function into a set of tasks and rendering them for a task‐based programming model. The package can be used directly through the R environment on parallel systems without the user needing any C , CUDA , or MPI knowledge. Currently, ExaGeoStatR supports several maximum likelihood computation variants such as exact, diagonal super tile and tile low‐rank approximations, and mixed‐precision. ExaGeoStatR also provides a tool to simulate large‐scale synthetic datasets. These datasets can help assess different implementations of the maximum log‐likelihood approximation methods. Herein, we show the implementation details of ExaGeoStatR , analyze its performance on various parallel architectures, and assess its accuracy using synthetic datasets with up to 250K observations. The experimental analysis covers the exact computation of ExaGeoStatR to demonstrate the parallel capabilities of the package. We provide a hands‐on tutorial to analyze a sea surface temperature real dataset. The performance evaluation involves comparisons with the popular packages GeoR , fields , and bigGP for exact Gaussian likelihood evaluation. The approximation methods in ExaGeoStatR are not considered in this article since they were analyzed in previous studies." @default.
• W4308451471 created "2022-11-12" @default.
• W4308451471 creator A5017526753 @default.
• W4308451471 creator A5021283893 @default.
• W4308451471 creator A5027166101 @default.
• W4308451471 creator A5037803788 @default.
• W4308451471 creator A5044386909 @default.
• W4308451471 creator A5087040072 @default.
• W4308451471 creator A5090118631 @default.
• W4308451471 date "2022-11-06" @default.
• W4308451471 modified "2023-10-14" @default.
• W4308451471 title "Large‐scale environmental data science with ExaGeoStatR" @default.
• W4308451471 cites W143236119 @default.
• W4308451471 cites W1524392826 @default.
• W4308451471 cites W1534349507 @default.
• W4308451471 cites W1695074111 @default.
• W4308451471 cites W1726639901 @default.
• W4308451471 cites W1726993684 @default.
• W4308451471 cites W1747046542 @default.
• W4308451471 cites W1767217354 @default.
• W4308451471 cites W1837874438 @default.
• W4308451471 cites W1853715423 @default.
• W4308451471 cites W1864722563 @default.
• W4308451471 cites W1867289061 @default.
• W4308451471 cites W1898904249 @default.
• W4308451471 cites W1972845114 @default.
• W4308451471 cites W1976520615 @default.
• W4308451471 cites W1978311333 @default.
• W4308451471 cites W1992597527 @default.
• W4308451471 cites W1998966727 @default.
• W4308451471 cites W2002591317 @default.
• W4308451471 cites W2004807582 @default.
• W4308451471 cites W2007873570 @default.
• W4308451471 cites W2013627498 @default.
• W4308451471 cites W2052077920 @default.
• W4308451471 cites W2056894411 @default.
• W4308451471 cites W2058753770 @default.
• W4308451471 cites W2060116069 @default.
• W4308451471 cites W2061320421 @default.
• W4308451471 cites W2087440962 @default.
• W4308451471 cites W2111188590 @default.
• W4308451471 cites W2111857777 @default.
• W4308451471 cites W2121893797 @default.
• W4308451471 cites W2124611503 @default.
• W4308451471 cites W2144898279 @default.
• W4308451471 cites W2145689771 @default.
• W4308451471 cites W2168612748 @default.
• W4308451471 cites W2269456039 @default.
• W4308451471 cites W2331024206 @default.
• W4308451471 cites W2464727076 @default.
• W4308451471 cites W2514303448 @default.
• W4308451471 cites W2785799516 @default.
• W4308451471 cites W2803588824 @default.
• W4308451471 cites W2962853524 @default.
• W4308451471 cites W2963513094 @default.
• W4308451471 cites W2964297483 @default.
• W4308451471 cites W2982906409 @default.
• W4308451471 cites W2998777207 @default.
• W4308451471 cites W3000508506 @default.
• W4308451471 cites W3099557005 @default.
• W4308451471 cites W3114272883 @default.
• W4308451471 cites W3122231250 @default.
• W4308451471 cites W3134488295 @default.
• W4308451471 cites W3140208069 @default.
• W4308451471 cites W3165254043 @default.
• W4308451471 cites W4229880569 @default.
• W4308451471 cites W4246367117 @default.
• W4308451471 cites W4254687493 @default.
• W4308451471 cites W4285100127 @default.
• W4308451471 cites W45374770 @default.
• W4308451471 cites W54587910 @default.
• W4308451471 doi "https://doi.org/10.1002/env.2770" @default.
• W4308451471 hasPublicationYear "2022" @default.
• W4308451471 type Work @default.
• W4308451471 citedByCount "1" @default.
• W4308451471 countsByYear W43084514712023 @default.
• W4308451471 crossrefType "journal-article" @default.
• W4308451471 hasAuthorship W4308451471A5017526753 @default.
• W4308451471 hasAuthorship W4308451471A5021283893 @default.
• W4308451471 hasAuthorship W4308451471A5027166101 @default.
• W4308451471 hasAuthorship W4308451471A5037803788 @default.
• W4308451471 hasAuthorship W4308451471A5044386909 @default.
• W4308451471 hasAuthorship W4308451471A5087040072 @default.
• W4308451471 hasAuthorship W4308451471A5090118631 @default.
• W4308451471 hasBestOaLocation W43084514712 @default.
• W4308451471 hasConcept C11413529 @default.
• W4308451471 hasConcept C173608175 @default.
• W4308451471 hasConcept C2778119891 @default.
• W4308451471 hasConcept C41008148 @default.
• W4308451471 hasConcept C45374587 @default.
• W4308451471 hasConcept C459310 @default.
• W4308451471 hasConcept C80444323 @default.
• W4308451471 hasConceptScore W4308451471C11413529 @default.
• W4308451471 hasConceptScore W4308451471C173608175 @default.
• W4308451471 hasConceptScore W4308451471C2778119891 @default.
• W4308451471 hasConceptScore W4308451471C41008148 @default.
• W4308451471 hasConceptScore W4308451471C45374587 @default.
• W4308451471 hasConceptScore W4308451471C459310 @default.
• W4308451471 hasConceptScore W4308451471C80444323 @default.
• W4308451471 hasFunder F4320322320 @default.

• next