Albany is an implicit, unstructured grid, finite element code for the solution and analysis of partial differential equations. Albany is the main demonstration application of the AgileComponents software development strategy at Sandia. It is a PDE code that strives to be built almost entirely from functionality contained within reusable libraries (such as Trilinos/STK/Dakota/PUMI). Albany plays a large role in demonstrating and maturing functionality of new libraries, and also in the interfaces and interoperability between these libraries. It also serves to expose gaps in our coverage of capabilities and interface design.
In addition to the component-based code design strategy, Albany also attempts to showcase the concept of Analysis Beyond Simulation, where an application code is developed up from for a design and analysis mission. All Albany applications are born with the ability to perform sensitivity analysis, stability analysis, optimization, and uncertainty quantification, with a clean interfaces for exposing design parameters for manipulation by analysis algorithms.
Albany also attempts to be a model for software engineering tools and processes, so that new research codes can adopt the Albany infrastructure as a good starting point. This effort involves a close collaboration with the 1400 SEMS team.
The Albany code base is host to several application projects, notably:
- LCM (Laboratory for Computational Mechanics) [PI J. Ostien]: A platform for research in finite deformation mechanics, including algorithms for failure and fracture modeling, discretizations, implicit solution algorithms, advanced material models, coupling to particle-based methods, and efficient implementations on new architectures.
- QCAD (Quantum Computer Aided Design) [PI Nielsen]: A code to aid in the design of quantum dots from built in doped silicon devices. QCAD solves the coupled Schoedinger-Poisson system. When wrapped in Dakota, optimal operating conditions can be found.
- FELIX (Finite Element for Land Ice eXperiments) [PI Salinger]: This application solves variants of a nonlinear Stokes flow for simulating the evolution of Ice Sheets. In particular, it conducts climate modeling of the Greenland and Antarctic Ice Sheets for Sea-Level Rise. Will be linked into ACME.
- Aeras [PI Spotz]: A component-based approach to atmospheric modeling, where advanced analysis algorithms and design for efficient code on new architectures are built into the code.
In addition, Albany is used as a platform for algorithmic research:
- FASTMath SciDAC project: We are developing a capability for adaptive mesh refinement within an unstructured grid application, in collaboration with Mark Shephard’s group at the SCOREC center at RPI.
- Embedded UQ: Research into embedded UQ algorithms led by Eric Phipps often uses Albany as a demonstration platform.
- Performance Portable Kernels for new architectures: Albany is serving as a research vehicle for programming finite element assembly kernels using the Trilinos/Kokkos programming model and library.
Transforming and decarbonizing the world’s energy systems to make them environmentally sustainable while maintaining high reliability and low cost is a task that requires the very best computational and simulation capabilities to examine a complete range of technology options, ensure that the best choices are made, and to support their rapid and effective implementation.
The Institute for Design of Advanced Energy Systems (IDAES) was originated to bring the most advanced modeling and optimization capabilities to these challenges. The resulting IDAES integrated platform utilizes the most advanced computational algorithms to enable the design and optimization of complex, interacting energy and process systems from individual plant components to the entire electrical grid.
IDAES is led by the National Energy Technology Laboratory (NETL) with participants from Lawrence Berkeley National Laboratory (LBNL), Sandia National Laboratories (SNL), Carnegie-Mellon University, West Virginia University, University of Notre Dame, and Georgia Institute of Technology.
The IDAES leadership team is:
- David C. Miller, Technical Director
- Anthony Burgard, NETL PI
- Deb Agarwal, LBNL PI
- John Siirola, SNL PI
MESQUITE is a linkable software library that applies a variety of node-movement algorithms to improve the quality and/or adapt a given mesh. Mesquite uses advanced smoothing and optimization to:
- Untangle meshes,
- Provide local size control,
- Improve angles, orthogonality, and skew,
- Increase minimum edge-lengths for increased time-steps,
- Improve mesh smoothness,
- Perform anisotropic smoothing,
- Improve surface meshes, adapt to surface curvature,
- Improve hybrid meshes (including pyramids & wedges),
- Smooth meshes with hanging nodes,
- Maintain quality of moving and/or deforming meshes,
- Perform ALE rezoning,
- Improve mesh quality on and near boundaries,
- Improve transitions across internal boundaries,
- Align meshes with vector fields, and
- R-adapt meshes to solutions using error estimates.
Mesquite improves surface or volume meshes which are structured, unstructured, hybrid, or non-comformal. A variety of element types are permitted. Mesquite is designed to be as efficient as possible so that large meshes can be improved.
Achieving practical exascale supercomputing will require massive increases in energy efficiency. The bulk of this improvement will likely be derived from hardware advances such as improved semiconductor device technologies and tighter integration, hopefully resulting in more energy efficient computer architectures. Still, software will have an important role to play. With every generation of new hardware, more power measurement and control capabilities are exposed. Many of these features require software involvement to maximize feature benefits. This trend will allow algorithm designers to add power and energy efficiency to their optimization criteria. Similarly, at the system level, opportunities now exist for energy-aware scheduling to meet external utility constraints such as time of day cost charging and power ramp rate limitations. Finally, future architectures might not be able to operate all components at full capability for a range of reasons including temperature considerations or power delivery limitations. Software will need to make appropriate choices about how to allocate the available power budget given many, sometimes conflicting considerations. For these reasons, we have developed a portable API for power measurement and control.
IO libraries typically focus on writing the entire simulation domain for each output. For many computation classes, this is the correct choice. However, there are some cases where this approach is wasteful in time and space.
The Stitch library was developed initially for use with the SPPARKS kinetic monte carlo simulation to handle IO tasks for a welding simulation. This simulation type has a particular feature where there is computational intensity in a small part of the simulation domain with the rest being idle. Given this intensity, only writing the area that changes is far more space efficient than writing the entire simulation domain for each output. Further, the computation can be focused strictly on the area where the data will change rather than the whole domain. This can yield a reduction from 1024 to 16 processes and 1/64th the data written. These combined can lead to a reduction in the computation time with no loss in data quality. If anything, by reducing the amount written each time, more output is possible.
This approach is also applicable for finite element codes that share the same localized physics.
The code is in the final stages of copyright review and will be released on github.com. A work in progress paper was presented at PDSW-DISCS @ SC18 and a full CS conference paper is planned for H1 2019 and a follow-on materials science journal paper.
Progressive data storage IO library. This library enables computing on a small part of the simulation domain at a time and then stitching together a coherent domain view based on a time epoch on request. Initial demonstration is for metal additive manufacturing. Paper at IPDPS 2020: DOI: 10.1109/IPDPS47924.2020.00016
The Zoltan project focuses on parallel algorithms for parallel combinatorial scientific computing, including partitioning, load balancing, task placement, graph coloring, matrix ordering, distributed data directories, and unstructured communication plans.
The Zoltan toolkit is an open-source library of MPI-based distributed memory algorithms. It includes geometric and hypergraph partitioners, global graph coloring, distributed data directories using rendezvous algorithms, primitives to simplify data movement and unstructured communication, and interfaces to the ParMETIS, Scotch and PaToH partitioning libraries. It is written in C and can be used as a stand-alone library.
The Zoltan2 toolkit is the next-generation toolkit for multicore architectures. It includes MPI+OpenMP algorithms for geometric partitioning, architecture-aware task placement, and local matrix ordering. It is written in templated C++ and is tightly integrated with the Trilinos toolkit.
