Hardware management¶
Arbor provides two ways for working with hardware resources:
Prescribe the hardware resources and their contexts for use in Arbor simulations.
Query available hardware resources (e.g. the number of available GPUs), and initializing MPI.
Note that to utilize some hardware features Arbor must be built and installed with the feature enabled, for example MPI or a GPU. Please refer to the installation guide for information on how to enable hardware support.
Available resources¶
Helper functions for checking cmake or environment variables, as well as configuring and checking MPI are the following:
- arbor.config()¶
Returns a dictionary to check which options the Arbor library was configured with at compile time:
ARB_MPI_ENABLED
ARB_WITH_MPI4PY
ARB_GPU_ENABLED
ARB_VECTORIZE
ARB_WITH_PROFILING
ARB_WITH_NEUROML
ARB_USE_BUNDLED_LIBS
ARB_VERSION
ARB_ARCH
import arbor arbor.config() {'mpi': True, 'mpi4py': True, 'gpu': False, 'vectorize': True, 'profiling': True, 'neuroml': True, 'bundled': True, 'version': '0.5.3-dev', 'arch': 'native'}
- arbor.mpi_init()¶
Initialize MPI with
MPI_THREAD_SINGLE
, as required by Arbor.
- arbor.mpi_is_initialized()¶
Check if MPI is initialized.
- class arbor.mpi_comm¶
- mpi_comm()¶
By default sets MPI_COMM_WORLD as communicator.
- mpi_comm(object)
Converts a Python object to an MPI Communicator.
- arbor.mpi_finalize()¶
Finalize MPI by calling
MPI_Finalize
.
- arbor.mpi_is_finalized()¶
Check if MPI is finalized.
Prescribed resources¶
The Python wrapper provides an API for:
prescribing which hardware resources are to be used by a simulation using
proc_allocation
.opaque handles to hardware resources used by simulations called
context
.
- class arbor.proc_allocation¶
Enumerates the computational resources on a node to be used for a simulation, specifically the number of threads and identifier of a GPU if available.
- proc_allocation([threads=1, gpu_id=None])¶
Constructor that sets the number of
threads
and the idgpu_id
of the available GPU.
- threads¶
The number of CPU threads available, 1 by default. Must be set to 1 at minimum.
- gpu_id¶
The identifier of the GPU to use. Must be
None
, or a non-negative integer.The
gpu_id
corresponds to theint device
parameter used by CUDA API calls to identify gpu devices. Set toNone
to indicate that no GPU device is to be used. SeecudaSetDevice
andcudaDeviceGetAttribute
provided by the CUDA API.
Here are some examples of how to create a
proc_allocation
.import arbor # default: one thread and no GPU selected alloc1 = arbor.proc_allocation() # 8 threads and no GPU alloc2 = arbor.proc_allocation(8, None) # reduce alloc2 to 4 threads and use the first available GPU alloc2.threads = 4 alloc2.gpu_id = 0
- class arbor.context¶
An opaque handle for the hardware resources used in a simulation. A
context
contains a thread pool, and optionally the GPU state and MPI communicator. Users of the library do not directly use the functionality provided bycontext
, instead they configure contexts, which are passed to Arbor interfaces for domain decomposition and simulation.- context(threads, gpu_id, mpi)
Create a context that uses a set number of
threads
and gpu identifiergpu_id
and MPI communicatormpi
for distributed calculation.- threads¶
The number of threads available locally for execution. Must be set to 1 at minimum. 1 by default. Passing
"avail_threads"
(as string) will query and use the maximum number of threads the system makes available.
- gpu_id¶
The identifier of the GPU to use,
None
by default. Must beNone
, or a non-negative integer. Can only be set when Arbor was built with GPU support.
- context(alloc, mpi)
Create a distributed context, that uses the local resources described by
proc_allocation
, and uses the MPI communicator for distributed calculation.- alloc¶
The computational resources, one thread and no GPU by default.
Contexts can be queried for information about which features a context has enabled, whether it has a GPU, how many threads are in its thread pool.
- has_gpu¶
Query whether the context has a GPU.
- has_mpi¶
Query whether the context uses MPI for distributed communication.
- threads¶
Query the number of threads in the context’s thread pool.
- ranks¶
Query the number of distributed domains. If the context has an MPI communicator, return is equivalent to
MPI_Comm_size
. If the communicator has no MPI, returns 1.
- rank¶
The numeric id of the local domain. If the context has an MPI communicator, return is equivalent to
MPI_Comm_rank
. If the communicator has no MPI, returns 0.
Here are some simple examples of how to create a
context
:import arbor import mpi4py.MPI as mpi # Construct a context that uses 1 thread and no GPU or MPI. context = arbor.context() # Construct a context that: # * uses 8 threads in its thread pool; # * does not use a GPU, reguardless of whether one is available # * does not use MPI. alloc = arbor.proc_allocation(8, None) context = arbor.context(alloc) # Construct a context that uses: # * 4 threads and the first GPU; # * MPI_COMM_WORLD for distributed computation. alloc = arbor.proc_allocation(4, 0) comm = arbor.mpi_comm(mpi.COMM_WORLD) context = arbor.context(alloc, comm)