Slam User Guide¶
Axom’s Set-theoretic Lightweight API for Meshes (SLAM) component provides high performance building blocks for distributed-memory mesh data structures in HPC simulation codes.
Simulation codes have a broad range of requirements for their mesh data structures, spanning the complexity gamut from structured Cartesian grids to fully unstructured polyhedral meshes. Codes also need to support features like dynamic topology changes, adaptive mesh refinement (AMR), submesh elements and ghost/halo layers, in addition to other custom features.
Slam targets the low level implementation of these distributed mesh data structures and is aimed at developers who implement mesh data structures within HPC applications.
Slam’s design is motivated by the observation that despite vast differences in the high level features of such mesh data structures, many of the core concepts are shared at a lower level, where we need to define and process mesh entities and their associated data and relationships.
Slam provides a simple, intuitive, API centered around a set-theoretic abstraction for meshes and associated data. Specifically, it models three core set-theoretic concepts:
- Sets of entities (e.g. vertices, cells, domains)
- Relations among a pair of sets (e.g. incidence, adjacency and containment relations)
- Maps defining fields and attributes on the elements of a given set.
The goal is for users to program against Slam’s interface without having to be aware of different design choices, such as the memory layout and underlying data containers. The exposed API is intended to feel natural to end users (e.g. application developers and domain scientists) who operate on the meshes that are built up from Slam’s abstractions.
See Core concepts for more details.
There is considerable variability in how these abstractions can be implemented and user codes make many different design choices. For example, we often need different data structures to support dynamic meshes than we do for static meshes. Similarly, codes might choose different container types for their arrays (e.g. STL vectors vs. raw C-arrays vs. custom array types).
Performance considerations can also come in to play. For example, in some cases, a code has knowledge of some fixed parameters (e.g. the stride between elements in a relation). Specifying this information at compile-time allows the compiler to better optimize the generated code than specifying it at runtime.
Slam uses a Policy-based design to orthogonally decompose the feature space without sacrificing performance. This makes it easier to customize the behavior of Slam’s sets, relations and maps and to extend support for custom features extend the basic interface.
See Policy-based design for more details.
- Slam is under active development with many features planned.
- Support for GPUs in Slam is under development.
- Slam’s policy-based design enable highly configurable classes which are explicitly defined via type aliases. We are investigating ways to simplify this set up using Generator classes where enumerated strings can define related types within a mesh configuration.