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Dive into the research topics where Aron J. Ahmadia is active.

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Featured researches published by Aron J. Ahmadia.


SIAM Journal on Scientific Computing | 2012

PYCLAW: ACCESSIBLE, EXTENSIBLE, SCALABLE TOOLS FOR WAVE PROPAGATION PROBLEMS "

David I. Ketcheson; Kyle T. Mandli; Aron J. Ahmadia; Amal Alghamdi; Manuel Quezada de Luna; Matteo Parsani; Matthew Knepley; Matthew Emmett

Development of scientific software involves tradeoffs between ease of use, generality, and performance. We describe the design of a general hyperbolic PDE solver that can be operated with the convenience of MATLAB yet achieves efficiency near that of hand-coded Fortran and scales to the largest supercomputers. This is achieved by using Python for most of the code while employing automatically wrapped Fortran kernels for computationally intensive routines, and using Python bindings to interface with a parallel computing library and other numerical packages. The software described here is PyClaw, a Python-based structured grid solver for general systems of hyperbolic PDEs [K. T. Mandli et al., PyClaw Software, Version 1.0, http://numerics.kaust.edu.sa/pyclaw/ (2011)]. PyClaw provides a powerful and intuitive interface to the algorithms of the existing Fortran codes Clawpack and SharpClaw, simplifying code development and use while providing massive parallelism and scalable solvers via the PETSc library. The...


SIAM Journal on Scientific Computing | 2013

Achieving Textbook Multigrid Efficiency for Hydrostatic Ice Sheet Flow

Jed Brown; Barry F. Smith; Aron J. Ahmadia

The hydrostatic equations for ice sheet flow offer improved fidelity compared with the shallow ice approximation and shallow stream approximation popular in todays ice sheet models. Nevertheless, they present a serious bottleneck because they require the solution of a three-dimensional (3D) nonlinear system, as opposed to the two-dimensional system present in the shallow stream approximation. This 3D system is posed on high-aspect domains with strong anisotropy and variation in coefficients, making it expensive to solve with current methods. This paper presents a Newton--Krylov multigrid solver for the hydrostatic equations that demonstrates textbook multigrid efficiency (an order of magnitude reduction in residual per iteration and solution of the fine-level system at a small multiple of the cost of a residual evaluation). Scalability on Blue Gene/P is demonstrated, and the method is compared to various algebraic methods that are in use or have been proposed as viable approaches.


international symposium on parallel and distributed computing | 2012

Scalable Force Directed Graph Layout Algorithms Using Fast Multipole Methods

Enas Yunis; Rio Yokota; Aron J. Ahmadia

We present an extension to ExaFMM, a Fast Multipole Method library, as a generalized approach for fast and scalable execution of the Force-Directed Graph Layout algorithm. The Force-Directed Graph Layout algorithm is a physics-based approach to graph layout that treats the vertices V as repelling charged particles with the edges E connecting them acting as springs. Traditionally, the amount of work required in applying the Force-Directed Graph Layout algorithm is O(|V|2 + |E|) using direct calculations and O(|V| log |V| + |E|) using truncation, filtering, and/or multi-level techniques. Correct application of the Fast Multipole Method allows us to maintain a lower complexity of O(|V| + |E|) while regaining most of the precision lost in other techniques. Solving layout problems for truly large graphs with millions of vertices still requires a scalable algorithm and implementation. We have been able to leverage the scalability and architectural adaptability of the ExaFMM library to create a Force-Directed Graph Layout implementation that runs efficiently on distributed multicore and multi-GPU architectures.


arXiv: Numerical Analysis | 2012

Optimal stability polynomials for numerical integration of initial value problems

David I. Ketcheson; Aron J. Ahmadia

We consider the problem of finding optimally stable polynomial approximations to the exponential for application to one-step integration of initial value ordinary and partial differential equations. The objective is to find the largest stable step size and corresponding method for a given problem when the spectrum of the initial value problem is known. The problem is expressed in terms of a general least deviation feasibility problem. Its solution is obtained by a new fast, accurate, and robust algorithm based on convex optimization techniques. Global convergence of the algorithm is proven in the case that the order of approximation is one and in the case that the spectrum encloses a starlike region. Examples demonstrate the effectiveness of the proposed algorithm even when these conditions are not satisfied.


Archive | 2017

Erdc/Proteus: 1.4.2

Chris Kees; Mfarthin; Aggelos S. Dimakopoulos; I. Akkerman; Tristan de Lataillade; Aron J. Ahmadia; Alistairbntl; Giovanni-Cozzuto; Nathan Neri; Timothypovich; Steven Mattis; Zhang-Alvin; Spencer Patty; Manuel-Quezada; T.J. Corona; Matt Malej; MajinSaha; Nehal J Wani; Wacyyang; Lmaurel; Yuxiang Lin; Robert Sawko; Min Rk; Leajenkins; Jed Brown; Gscovaz

adds some fixes to entropy viscosity scheme for Shallow Water Equations fixes dependency checking on mprans modules


PeerJ | 2016

The Clawpack 5.x software

Kyle T. Mandli; Aron J. Ahmadia; Marsha J. Berger; Donna A. Calhoun; David L. George; Yiannis Hadjimichael; David I. Ketcheson; Grady I. Lemoine; Randall J. LeVeque

1 Clawpack is a software package designed to solve nonlinear hyperbolic partial differential equa2 tions using high-resolution finite volume methods based on Riemann solvers and limiters. The pack3 age includes a number of variants aimed at different applications and user communities. Clawpack 4 has been actively developed as an open source project for over 20 years. The latest major release, 5 Clawpack 5, introduces a number of new features and changes to the code base and a new devel6 opment model based on GitHub and Git submodules. This article provides a summary of the most 7 significant changes, the rationale behind some of these changes, and a description of our current 8 development model. 9


PeerJ | 2016

Clawpack: building an open source ecosystem for solving hyperbolic PDEs

Kyle T. Mandli; Aron J. Ahmadia; Marsha J. Berger; Donna A. Calhoun; David L. George; Yiannis Hadjimichael; David I. Ketcheson; Grady I. Lemoine; Randall J. LeVeque


ieee international conference on high performance computing data and analytics | 2011

PetClaw: a scalable parallel nonlinear wave propagation solver for Python

Amal Alghamdi; Aron J. Ahmadia; David I. Ketcheson; Matthew G. Knepley; Kyle T. Mandli; Lisandro Dalcin


Archive | 2016

petsc4py: The Python interface to PETSc

Lisandro Dalcin; Michael Lange; Garth N. Wells; Aron J. Ahmadia; Simon W. Funke; Asbjørn Nilsen Riseth; nocollier; Patrick E. Farrell; Matthew Knepley; Miklós Homolya; Jonathan Guyer; Jed Brown; David A. Ham; Jorge Cañardo Alastuey; Thomas Hisch; Lawrence Mitchell; Dmitry Karpeyev; Barry Smith


Archive | 2010

Achieving textbook multigrid eciency for hydrostatic ice sheet ow

Jed Brown; Barry F. Smith; Aron J. Ahmadia

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David I. Ketcheson

King Abdullah University of Science and Technology

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Jed Brown

Argonne National Laboratory

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Amal Alghamdi

King Abdullah University of Science and Technology

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Lisandro Dalcin

King Abdullah University of Science and Technology

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Barry F. Smith

Argonne National Laboratory

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Dmitry Karpeyev

Argonne National Laboratory

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David A. Ham

Imperial College London

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