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Portage: A Modular Data Remap Library for Multiphysics Applications on Advanced Architectures


Angela Herring ,

Lagrangian codes, Los Alamos National Laboratory, US
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Charles Ferenbaugh,

Applied computer science, Los Alamos National Laboratory, US
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Christopher Malone,

Lagrangian codes, Los Alamos National Laboratory, US
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Daniel Shevitz,

Applied computer science, Los Alamos National Laboratory, US
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Evgeny Kikinzon,

Applied computer science, Los Alamos National Laboratory, US
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Gary Dilts,

Computational physics and methods, Los Alamos National Laboratory, US
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Hoby Rakotoarivelo,

Applied mathematics and plasma physics, Los Alamos National Laboratory, US
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Jan Velechovsky,

Eulerian codes, Los Alamos National Laboratory, US
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Konstantin Lipnikov,

Applied mathematics and plasma physics, Los Alamos National Laboratory, US
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Navamita Ray,

Applied computer science, Los Alamos National Laboratory, US
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Rao Garimella

Applied mathematics and plasma physics, Los Alamos National Laboratory, US
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Portage is a scalable and extensible remap library for numerical simulations. It supports state-of-the-art remap schemes for meshes and particles in 2D and 3D up to a second-order accuracy. Portage ensures critical properties such as local/global conservation and bounds preservation for mesh remap. It enables multi-material field remap through a dedicated plugin, and leverages the hybrid parallelism exposed by advanced architectures using multi-processing and multi-threading.

How to Cite: Herring, A., Ferenbaugh, C., Malone, C., Shevitz, D., Kikinzon, E., Dilts, G., Rakotoarivelo, H., Velechovsky, J., Lipnikov, K., Ray, N. and Garimella, R., 2021. Portage: A Modular Data Remap Library for Multiphysics Applications on Advanced Architectures. Journal of Open Research Software, 9(1), p.26. DOI:
  Published on 28 Sep 2021
 Accepted on 12 Mar 2021            Submitted on 26 Jun 2020

(1) Overview


Remap is the transfer of numerical fields from a computational domain to another. It is said to be conservative when some extensive quantity is preserved during this transfer. For instance, one may want to preserve the material mass while remapping the density field defined on a source mesh to a target mesh. Remap is necessary for:

  • interpolating fields from a distorted mesh to an improved one in an indirect Arbitrary Lagrangian-Eulerian simulation (ALE in short).
  • linking internal and external fields in a multi-physics simulation pipeline.
  • interpolating data from different numerical codes.

Conservative schemes have long been of particular interest for ALE simulations [1]. In such simulations, the mesh is allowed to evolve in time along with the material such as depicted in Figure 1. In that case, the mesh is smoothed to prevent cells distorting or tangling, and all fields computed on the old mesh are remapped to the new one. Remap schemes such as advection-based and intersection-based remaps [2] are often integrated within ALE hydrodynamics codes such as FLAG [3]. They are also useful for other multi-physics applications such as Amanzi [4] (to assimilate scattered input data from observation sources), and for code-to-code linking problems such as in Ingen [5]. However, this tight integration has led to a proliferation of remap schemes that cannot be easily shared between simulation codes. To address this issue, standalone conservative remap software has been developed such as the closed source Overlink [6] from Lawrence Livermore National Laboratory, the legacy REMAP3D code [7] from Los Alamos National Laboratory, or the globally conservative open-source DTK code [8] from Oak Ridge National Laboratory.

Figure 1 

Mesh deformation in an ALE simulation [1].

Portage is currently the only actively developed open source library that performs locally conservative remap. It provides a lightweight and extensible interface that can easily be customized and integrated into simulation codes. Portage supports general polyhedral mesh fields remap up to a second-order accuracy, while preserving integral quantities of interest and numerical bounds. It supports remap between particle fields as well, and provides means to perform mesh remap using the particle remap engine. Portage is designed to scale to thousands of cores on distributed architectures through MPI and OpenMP (using Nvidia’s Thrust wrapper).

Implementation and architecture


Portage supports three types of remap:

  • Intersection-based remap is a conservative scheme that relies on exact intersection of source and target meshes. It first identifies the candidate source cells that may potentially overlap each target cell. It then computes two moments of intersection (volume and centroid) between each target cell and overlapping source cells (Figure 2). Finally, it interpolates the target cell value from the candidate source cells values using the moments of intersection as weights [2].
  • Advection-based remap is a conservative scheme specifically designed for meshes with the same topology but with different node positions. As described earlier, this need arises from ALE hydrodynamic simulations when the mesh is slightly smoothed to prevent cell distortion induced by the Lagrangian fluid motion. Here, the remap is formulated as an advection or fluxing of integral quantities in/out of each cell through its faces. Any quantity that is fluxed out of a cell is added into one of its neighbors, so the method is intrinsically conservative. In this algorithm, the interpolation weights are deduced from the flux volumes, which is less expensive – but less accurate – than the previous remap scheme [9].
  • Particle remap is a specific scheme for point clouds. In this method, source fields are reconstructed by means of local regression [10]. Here a shape function is attached to each source point (scatter form) or each target point (gather form). The algorithm first identifies the source points included in the support of the shape function of a target point (Figure 3) which are included in the zone delimited by the user-defined smoothing lengths which control the number of points used for the local regression. It then computes the weights by evaluating the shape function and its derivatives on each point. Finally, it approximates the value on each target point using those weights. Despite its high accuracy, this remap method is not conservative.

Each step can be processed in parallel with the granularity of a single point or cell.

Figure 2 

Illustration of intersection-based remap.

Figure 3 

Illustration of particle-based remap.


Portage has a modular design. It relies on extensive C++ templating of all remap steps, allowing client codes to extend, adapt or replace them by customized ones. Besides, most of its core methods are designed to have no side-effects to ease their parallelization and their individual reuse. Portage’s components and their interactions are given in Figure 4.

Figure 4 

Portage software design and workflow.

Portage takes the source and target domains along with fields data as inputs, and then outputs remapped fields on the target domain. Here a domain can be a mesh or a point cloud. For multi-material fields, it requires the material volume fractions on the source domain as depicted in Figure 5, and which corresponds to the proportion of each material on each cell. The remap workflow consists of six stages:

Figure 5 

Additional step involved in multi-material remap.

  1. Redistribution: this optional step is only necessary for distributed domains with a mismatch between the source and target partitions. In that case, some source entities (points or cells) are reassigned among MPI ranks such that each target subdomain is overlapped by the corresponding source subdomain. This eliminates the need for communications in the remaining steps.
  2. Interface reconstruction: this optional step is only required for multi-material fields and is performed by a dedicated plugin called Tangram. It recovers the interface between different materials by computing the material polygons on each source cell, given their volume fractions and, optionally, their centroids for a second-order remap accuracy.
  3. Search: this step identifies and retrieves the source entities that are necessary to interpolate the value of a given target entity. The algorithm depends on the remap scheme:
    • intersection: collects the source cells that may overlap the target cell.
    • advection: collects the source cell itself and a subset of its neighbors.
    • particle: collects the source points included in the support of the shape function of a target point in scatter form, and vice-versa for gather form.
  4. Computation of weights: this step computes the contribution weights of each identified source entity to reconstruct the value on a given target entity. Again, the algorithm depends on the remap scheme:
    • intersection: computes the moments of intersection (volume and centroids) of each candidate source cell that overlaps the target cell.
    • advection: computes the moments of each swept polyhedron (volume and centroids) formed by the displacement of each face of the source cell.
    • particle: computes and accumulates the values of the shape functions and their derivatives on each point given by the search step.
  5. Interpolation: this step reconstructs the target entity values by interpolating them using the computed weights. For mesh remap, the gradient of the source field is required to achieve a second-order accurate reconstruction. It is computed in Portage by a least-squares method. Here, values can be limited using Barth-Jespersen’s limiter [11], except at domain boundaries because boundary conditions are not yet supported. For particles, we use the term estimation as recovered values may pass near the data not necessarily through it.
  6. Repair: this step is only necessary in case of mismatch between source and target mesh boundaries. Here, remapped values are fixed to enforce the conservation of integral quantities. Portage exposes three options to fix partially overlapped cells:
    • constant-preserving: no field value perturbations but not conservative.
    • locally-conservative: conservative but perturbations may occur: constant fields may not remain constant.
    • shifted-conservative: conservative with minimal perturbations but values are shifted: constant field remains constant but with a different value.

It is also possible to extrapolate values to empty cells in the target mesh.


A driver is the interface that exposes the remap capabilities to the client simulation code. Writing a driver allows client codes to mix, match, or extend specialized remap components for their particular needs. Portage comes with a few drivers to ease the design of custom ones and several apps to show common remap use cases. Each driver is templated on core components (interface reconstruction, search, weight computation, interpolation) for each remap method (intersection, advection, particle) and on mesh type. If the simulation code provides a mesh with a set of queries that conforms with the mesh wrapper interface, then no data recopy is involved. Portage embeds five built-in drivers:

  • uberdriver: an easy to use mesh remap class.
  • coredriver: a low-level mesh driver that allows finer control on remap steps.
  • mmdriver: a legacy monolithic mesh remap driver.
  • driver_swarm: a dedicated particle remap driver.
  • driver_mesh_swarm_mesh: a mesh remap driver that relies on particle kernels.

A basic example of a single-material mesh remap using coredriver methods (using default parameters where possible) is given in Listing 1. A list of available options for remap components is given in Table 1. Each of them is templated on source and target domains as well as field entity types.

Listing 1 

Example of using a driver for remap.

Table 1

Driver options for remap steps.


SEARCH SearchSimple search using bounding-box (2D)
SearchKDTree search using a k-d tree
SearchSimplePoints basic quadratic search for particles
SearchPointsByCells linear search for particles using a virtual cell

WEIGHTS IntersectRnD compute exact n-polytopes intersection
IntersectSweptFace compute moments of advected regions
Accumulate evaluate and sum shape functions/derivatives
options: shape kernels and geometry, basis, estimators

INTERPOLATE Interpolate_1stOrder first-order interpolation of mesh values
Interpolate_2ndOrder second-order interpolation with limiters
Estimate n-order approximation of particle values


Portage is designed for high performance computing clusters. It relies on both MPI and OpenMP to leverage the hybrid parallelism exposed by such architectures. Here we present some scaling results on a simple multi-material problem in Figure 6. Tests are run on a cluster formed by 256 dual-socket nodes (Intel Broadwell with 18 cores per-socket at 2.1 Ghz). Here we consider a cell-centered three-material field remap with 3D cartesian grids and a simple t-junction material distribution on the domain. The source and target grids have 403 and 1203 cells respectively. To ease memory pressure, we set a single MPI rank per node and 16 threads per rank explicitly pinned on cores using KMP_AFFINITY=granularity=core,compact.

Figure 6 

Scaling of multi-material remap in a hybrid parallel setting.

The total execution time and the remap time are depicted in black and red respectively. The time spent on material interface reconstruction – which is only performed on multi-material cells – is shown in purple. Here, the workload per rank is impacted by the uneven distribution of multi-material cells. Despite the workload imbalance, a reasonable scaling is still achieved.

Quality control

Portage is tested on Linux with Gnu and Intel compilers. It provides over 200 unit and functional tests as part of a Travis continuous integration setup using the Github workflow. In particular, they ensure that remap algorithms:

  • are bounds preserving,
  • provide the expected order of accuracy,
  • are conservative.

The code coverage in the latest release is 67% as shown in Figure 7.

Figure 7 

Code coverage in latest release.

(2) Availability

Operating system

Portage is designed for high performance computing clusters. Hence it is primarily targetted to Linux.

Programming language

Portage is written in C++14.

Additional system requirements





List of contributors

All contributors are or were affiliated with Los Alamos National Laboratory.

  • current: Angela Herring, Christopher Malone, Daniel Shevitz, Evgeny Kikinzon, Hoby Rakotoarivelo, Jan Velechovsky, Konstantin Lipnikov, Navamita Ray and Rao Garimella.
  • previous: Brendan Krueger, Charles Ferenbaugh, Christopher Sewell, Gary Dilts, Ondřej Čertí́k, Michael Rogers and Rachel Ertl.

Software location


Name: Portage

Persistent identifier:


Publisher: Angela Herring

Version published: 2.2.3

Date published: 27/04/2020

DOI: 10.5281/zenodo.4571000

Code repository

Name: Portage

Persistent identifier:


Date published: 01/09/2017

Support: We will use the GitHub “issues” feature as well as email ( to maintainers for support.



(3) Reuse potential

Portage is an extensible and mesh-agnostic library. Its unique design allows it to be re-used in a variety of applications such as:

  • field remap in ALE simulations,
  • multi-physics code-to-code field remap,
  • operator-split intra-code linking.

Portage is actively developed, supported and continuously released. Bugs and feature requests can be notified using the issue tracker on Github, as well as any question related to the software. User support may be reached by email at We welcome community contributions through pull requests.


We thank Benjamin Bergen, Brian Jean, Christoph Junghans, Davis Herring, Marc Charest and Mikhail Shashkov from Los Alamos National Laboratory for their help.

Funding statement

This work is supported by the U.S. Department of Energy (DoE) through Los Alamos National Laboratory which is operated by Triad National Security, LLC for the National Nuclear Security Administration of the DoE under contract 89233218CNA000001.

Competing interests

The authors have no competing interests to declare.


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