Hardware context

An Arbor simulation requires a Recipes, a (hardware) context, and a Domain decomposition. The Recipe contains the neuroscientific model, the hardware context describes the computational resources you are going to execute the simulation on, and the domain decomposition describes how Arbor will use the hardware. Since the context and domain decomposition may seem closely related at first, it might be instructive to see how recipes are used by Arbor:

Recipe
Cell descriptions
gid 1
type lif_cell_0
...
...
gid 37
type cable_cell_A
Morphology
Decor
Mechanisms
Connection sites
Label dictionary
Cell kinds
gid 1
type arbor.lif_cell
...
gid 37
type arbor.cable_cell
...
Simulator: requests cell description of gid=37
Recipe: type=cable_cell_A.
Simulator: requests cell kind of gid=37
Recipe: kind=arbor.cable_cell.
Simulator: look up cell group implementation for kind arbor.cable_cell.
Domain decomposition: cable_cell_group_gpu
Simulator: cable_cell_group_gpu construct cable_cell_A object.
cable_cell_group_gpu: Construction complete.
Simulator: cable_cell_group_gpu simulate for t .. t + dt
Simulation
Recipe
Context
12 threads
1 GPU
Domain decomposition
cable_cell_group_gpu
1 GPU
...
An illustration of the cell-specific portion of the recipe, and how it is used during the lifetime of the simulation: the simulation object will, depending on its configuration, query the recipe for the neuroscientific components it describes. This demonstration also show why the recipe separates cell descriptions from cell types. The latter is, as you might expect, shorthand, and is used in the allocation of the cell to a particular cell group. A cell group implementation is a handler for a certain kind of cell, and Arbor comes with these for all it's included cell kinds. However, users can develop their own specialized cell group implementations. More on that in the internal developer documentation.

Local resources are locally available computational resources, specifically the number of hardware threads and the number of GPUs.

An allocation enumerates the computational resources to be used for a simulation, typically a subset of the resources available on a physical hardware node.

New users can find using contexts a little verbose. The design is very deliberate, to allow fine-grained control over which computational resources an Arbor simulation should use. As a result Arbor is much easier to integrate into workflows that run multiple applications or libraries on the same node, because Arbor has a direct API for using on node resources (threads and GPU) and distributed resources (MPI) that have been partitioned between applications/libraries.

Execution context

An execution context contains the local thread pool, and optionally the GPU state and MPI communicator, if available. Users of the library configure contexts, which are passed to Arbor methods and types.

API