Bempp-cl: A fast Python based just-in-time compiling boundary element library.

Betcke, Timo; Scroggs, Matthew W.

doi:10.21105/joss.02879

Cited by 44 publications

(35 citation statements)

References 11 publications

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“…We have recently integrated exafmm-t with Bempp-cl, an open-source boundary element method (BEM) package in Python (Betcke & Scroggs, 2021), whose predecessor, BEM++ (Śmigaj et al, 2015), has enabled many acoustic and electromagnetic applications. In BEM applications, computations are dominated by the dense matrix-vector multiplication (matvec) in each iteration.…”

Section: Applicationmentioning

confidence: 99%

ExaFMM: a high-performance fast multipole method library with C++ and Python interfaces

Wang¹,

Yokota²,

Barba³

2021

JOSS

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Section: Applicationmentioning

confidence: 99%

ExaFMM: a high-performance fast multipole method library with C++ and Python interfaces

Wang¹,

Yokota²,

Barba³

2021

JOSS

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“…An introduction to Bempp Bempp [37] with Aij = Γ Ψi(r) Γ g(r, r )φj(r )ds(r )ds(r) and bi = Γ ψi(r)f (r)ds(r). Here, the functions Ψj are a finite dimensional basis of n test functions and the φj are a finite dimensional basis of n trial functions with the Galerkin solution being defined as φ = i xjφj.…”

Section: Boundary Integral Formulation Of Electrostatics In Molecular...mentioning

confidence: 99%

High-productivity, high-performance workflow for virus-scale electrostatic simulations with Bempp-Exafmm

Wang¹,

Cooper²,

Betcke³

et al. 2021

Preprint

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Biomolecular electrostatics is key in protein function and the chemical processes affecting it. Implicit-solvent models expressed by the Poisson-Boltzmann (PB) equation can provide insights with less computational power than full atomistic models, making large-system studies-at the scale of viruses, for example-accessible to more researchers. This paper presents a high-productivity and high-performance computational workflow combining Exafmm, a fast multipole method (FMM) library, and Bempp, a Galerkin boundary element method (BEM) package. It integrates an easy-to-use Python interface with well-optimized computational kernels that are written in compiled languages. Researchers can run PB simulations interactively via Jupyter notebooks, enabling faster prototyping and analyzing. We provide results that showcase the capability of the software, confirm correctness, and evaluate its performance with problem sizes between 8,000 and 2 million boundary elements. A study comparing two variants of the boundary integral formulation in regards to algebraic conditioning showcases the power of this interactive computing platform to give useful answers with just a few lines of code. As a form of solution verification, mesh refinement studies with a spherical geometry as well as with a real biological structure (5PTI) confirm convergence at the expected 1/N rate, for N boundary elements. Performance results include timings, breakdowns, and computational complexity. Exafmm offers evaluation speeds of just a few seconds for tens of millions of points, and O(N ) scaling. This allowed computing the solvation free energy of a Zika virus, represented by 1.6 million atoms and 10 million boundary elements, at 80-min runtime on a single compute node (dual 20-core Intel Xeon Gold 6148). All results in the paper are presented with utmost care for reproducibility. Readers can find input data (meshes and pqr files) in a Zenodo archive, Jupyter notebooks and secondary data necessary to reproduce all the figures in the paper's GitHub repository at https://github.com/barbagroup/bempp_exafmm_paper/, and all the software in version-controlled repositories under permissive standard licenses.

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“…At the end of 2019, we completed the main steps of a full rewrite of Bempp and released the first version (0.1) of Bempp-cl. This was followed later in 2020 by version 0.2, the first release that we considered feature complete and mature for application use [1]. Since then we have used Bempp-cl in a number of practical applications and many of our users are migrating to it from the old C++ based Bempp.…”

Section: The Bempp Boundary Element Librarymentioning

confidence: 99%

Designing a High-Performance Boundary Element Library With OpenCL and Numba

Betcke

Scroggs

2021

Comput. Sci. Eng.

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The Bempp boundary element library is a well known library for the simulation of a range of electrostatic, acoustic and electromagnetic problems in homogeneous bounded and unbounded domains. It originally started as a traditional C++ library with a Python interface. Over the last two years we have completely redesigned Bempp as a native Python library, called Bempp-cl, that provides computational backends for OpenCL (using PyOpenCL) and Numba. The OpenCL backend implements kernels for GPUs and CPUs with SIMD optimization. In this paper, we discuss the design of Bempp-cl, provide performance comparisons on different compute devices, and discuss the advantages and disadvantages of OpenCL as compared to Numba.

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Bempp-cl: A fast Python based just-in-time compiling boundary element library.

Cited by 44 publications

References 11 publications

ExaFMM: a high-performance fast multipole method library with C++ and Python interfaces

ExaFMM: a high-performance fast multipole method library with C++ and Python interfaces

High-productivity, high-performance workflow for virus-scale electrostatic simulations with Bempp-Exafmm

Designing a High-Performance Boundary Element Library With OpenCL and Numba

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