Insertion of PETSc in the NEMO stack software driving NEMO towards exascale computing

D’Amore, Luisa; Murli, A.; Boccia, Vania; Carracciuolo, Luisa

doi:10.1109/hpcsim.2014.6903761

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…In the work of Helfenstein and Koko, 22 the authors analyzed an SSOR preconditioned conjugate gradient algorithm on GPUs, focusing on the numerical solution of the generalized Poisson equation. In other works, [23][24][25] the authors discussed performance of methods, algorithms, and software for oceanography, which are based on the use of quasi-Newton methods for solution of "A Minimum Problem" in hybrid computing environments.…”

Section: Related Workmentioning

confidence: 99%

Implementation of a non‐linear solver on heterogeneous architectures

Carracciuolo

Lapegna

2018

Concurrency and Computation

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Heterogeneous architectures seem to be not only the present but also the future of the HPC world (eg, see the Exascale Project of U.S. Department of Energy or the European Horizon 2020 FET Proactive -High Performance Computing Call). A lot of work has been done in developing software libraries useful to solve problem described by linear equations on such computing systems.Instead, not the same effort is spent in such context for the implementation of software modules to be used to solve non-linear problem. In this work, we present some experiences related with the implementation of a Quasi-Newton method able to exploit, using a combination of "Task Scheduling," "matrix-free," and "look-ahead" approaches, both the CPUs and the GP-GPUs components of a heterogeneous system.

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Section: Related Workmentioning

confidence: 99%

Implementation of a non‐linear solver on heterogeneous architectures

Carracciuolo

Lapegna

2018

Concurrency and Computation

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“…Modeling the ocean sub‐mesoscale is one of the most important current challenges in physical oceanographic research . This is characterized by horizontal scale of 1‐10km and timescales of hours‐to‐days.…”

Section: Introductionmentioning

confidence: 99%

“…The deployment of application codes by means of the use of scientific libraries, such as PETSc (the Portable, Extensible Toolkit for Scientific computing), can be considered a good investment to maximize the availability of PinT algorithms to science.…”

Section: Introductionmentioning

confidence: 99%

A PETSc parallel‐in‐time solver based on MGRIT algorithm

Mele

Constantinescu

Carracciuolo

et al. 2018

Concurrency and Computation

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We address the development of a modular implementation of the MGRIT (MultiGrid-In-Time) algorithm to solve linear and nonlinear systems that arise from the discretization of evolutionary models with a parallel-in-time approach in the context of the PETSc (the Portable, Extensible Toolkit for Scientific computing) library. Our aim is to give the opportunity of predicting the performance gain achievable when using the MGRIT approach instead of the Time Stepping integrator (TS). To this end, we analyze the performance parameters of the algorithm that provide a-priori the best number of processing elements and grid levels to use to address the scaling of MGRIT, regarded as a parallel iterative algorithm proceeding along the time dimension.

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