The State of Fortran

Aradi, Bálint; Čertı́k, Ondřej; Curcic, Milan; Ehlert, Sebastian; Engel, Philipp; Goswami, Rohit; Hirsch, Michael; Lozada-Blanco, Asdrubal; Magnin, Vincent; Markus, Arjen; Pagone, Emanuele; Pribec, Ivan; Richardson, Brad; Snyder, H.J.; Une, Carlos; Urban, John; Vandenplas, Jérémie

doi:10.1109/mcse.2022.3159862

Cited by 19 publications

(17 citation statements)

References 11 publications

(12 reference statements)

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“…acknowledge Research Computing at Arizona State University for providing HPC and storage resources that have contributed to the research results reported within this paper. R.C also acknowledge valuable help received from Fortran-lang community [36,37]. In particular, R.C.…”

Section: Declarationsmentioning

confidence: 97%

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RPEM: Randomized Monte Carlo Parametric Expectation Maximization Algorithm

Chen¹,

Schumitzky²,

Kryshchenko³

et al. 2022

Preprint

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Inspired from quantum Monte Carlo methods, we developed a novel, fast, accurate, robust, and generalizable high performance algorithm for Monte Carlo Parametric Expectation Maximization (MCPEM) methods. We named it Randomized Parametric Expectation Maximization (RPEM). RPEM can be used on a personal computer as an independent engine or can serve as a 'booster' to be combined with MCPEM engines used in current population modeling software tools. RPEM can also run on supercomputer clusters, since it is fully parallelized and scalable.

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Section: Declarationsmentioning

confidence: 97%

“…(31) and Eq. (37) as examples, each of the subject i and mixing k can have way more than 100 times to be proposed and judged almost for free since the samples have already been stored at E-step, and wether they are accepted or not, are totally judged by the Metropolis method based on their importance, as indicated by not only the ratio of…”

Section: Recap Of Rpemmentioning

confidence: 99%

RPEM: Randomized Monte Carlo Parametric Expectation Maximization Algorithm

Chen¹,

Schumitzky²,

Kryshchenko³

et al. 2022

Preprint

View full text Add to dashboard Cite

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“…NPSA is written in high performance modern Fortran and equipped with MPI [49,50]. We ran NPSA on the Agave supercomputer cluster at Arizona State University (ASU) and tested its parallel scalability for the Voriconazole model and data.…”

Section: Scalability Of Npsa For Voriconazole Model and Datamentioning

confidence: 99%

NPSA: Nonparametric Simulated Annealing for Global Optimization

Chen¹,

Schumitzky²,

Kryshchenko³

et al. 2023

Preprint

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In this paper we describe NPSA, the first parallel nonparametric global maximum likelihood optimization algorithm using simulated annealing (SA). Unlike the nonparametric adaptive grid search method NPAG, which is not guaranteed to find a global optimum solution, and may suffer from the curse of dimensionality, NPSA is a global optimizer and it is free from these grid related issues. We illustrate NPSA by a number of examples including a pharmacokinetics (PK) model for Voriconazole and show that NPSA may be taken as an upgrade to the current grid search based nonparametric methods.

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“…To ensure a sustainable path for future Fortran code development, a vibrant community of developers at varying educational and career stages has undertaken an effort to grow and modernize the Fortran ecosystem [11], including extending the application of the language into non-traditional domains such as software package management [12]. Among the other many signs of new growth in the Fortran world is the increase in the number of production Fortran compiler projects over the past 5 years.…”

Section: A Why Fortran Mattersmentioning

confidence: 99%

Caffeine: CoArray Fortran Framework of Efficient Interfaces to Network Environments

Rouson

Bonachea

2022

2022 IEEE/ACM Eighth Workshop on the LLVM Compiler Infrastructure in HPC (LLVM-HPC)

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This paper provides an introduction to the CoArray Fortran Framework of Efficient Interfaces to Network Environments (Caffeine), a parallel runtime library built atop the GASNet-EX exascale networking library. Caffeine leverages several non-parallel Fortran features to write type-and rankagnostic interfaces and corresponding procedure definitions that support parallel Fortran 2018 features, including communication, collective operations, and related services. One major goal is to develop a runtime library that can eventually be considered for adoption by LLVM Flang, enabling that compiler to support the parallel features of Fortran.The paper describes the motivations behind Caffeine's design and implementation decisions, details the current state of Caffeine's development, and previews future work. We explain how the design and implementation offer benefits related to software sustainability by lowering the barrier to user contributions, reducing complexity through the use of Fortran 2018 C-interoperability features, and high performance through the use of a lightweight communication substrate.

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The State of Fortran

Cited by 19 publications

References 11 publications

RPEM: Randomized Monte Carlo Parametric Expectation Maximization Algorithm

RPEM: Randomized Monte Carlo Parametric Expectation Maximization Algorithm

NPSA: Nonparametric Simulated Annealing for Global Optimization

Caffeine: CoArray Fortran Framework of Efficient Interfaces to Network Environments

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