Benchpark Modifiers

In Benchpark, a modifier follows the Ramble Modifier and is an abstract object that can be applied to a large set of reproducible specifications. Modifiers are intended to encapsulate reusable patterns that perform a specific configuration of an experiment. This may include injecting performance analysis or setting up system resources.

Affinity: Threads, GPUs

We are using (with permission) the following implementation of Linux thread and GPU affinity checks. The following checks are possible:

affinity.mpi : for testing thread affinity of each rank in an MPI job
affinity.rocm : for testing AMD GPU affinity of each rank an MPI job
affinity.cuda : for testing NVIDIA GPU affinity of each rank in an MPI job

To use the Affinity modifier:

When you initialize your experiment, add affinity=on to experiment init.
A small, separate run will execute in your allocation to record the information.
The Affinity modifier will output a text file in the experiment directory which will look like this for benchpark experiment init --dest=saxpy --system=system-name saxpy+openmp affinity=on on 8 ranks, 2 threads/proc, 1 node:

affinity.mpi.out

affinity test for 8 MPI ranks
rank      0 @ dane1514
  thread   0 on cores [112]
  thread   1 on cores [119]
rank      1 @ dane1514
  thread   0 on cores [126]
  thread   1 on cores [133]
rank      2 @ dane1514
  thread   0 on cores [140]
  thread   1 on cores [147]
rank      3 @ dane1514
  thread   0 on cores [154]
  thread   1 on cores [161]
rank      4 @ dane1514
  thread   0 on cores [168]
  thread   1 on cores [175]
rank      5 @ dane1514
  thread   0 on cores [182]
  thread   1 on cores [189]
rank      6 @ dane1514
  thread   0 on cores [196]
  thread   1 on cores [203]
rank      7 @ dane1514
  thread   0 on cores [210]
  thread   1 on cores [217]

If also running with the caliper modifier, affinity information will be included in the Caliper metadata.

Caliper: Profiling

Caliper is an instrumentation and performance profiling library. We have implemented a Caliper modifier to enable profiling of Caliper-instrumented benchmarks in Benchpark.

To turn on profiling with Caliper, add caliper=<caliper_variant> to the experiment init setup step:

benchpark experiment init --dest=</path/to/experiment_root> --system=</path/to/system> <benchmark> caliper=<caliper_variant>

Valid values for <caliper_variant> are found in the Caliper Variant column of the table below. Benchpark will link the experiment to Caliper, and inject appropriate Caliper configuration at runtime. After the experiments in the workspace have completed running, a .cali file is created which contains the collected performance metrics.

An experiment must inherit from the Caliper experiment class to make use of the Caliper functionality. Most existing experiments should already do this, but if adding to a new experiment, it is as simple as adding it to the class definition signature. For example:

class Amg2023(Experiment, Caliper):

Allocation: Resources

Given:

an experiment that requests resources (nodes, cpus, gpus, etc.), and

a specification of the resources available on the system (cores_per_node, gpus_per_node, etc.),

the Allocation Modifier generates the appropriate scheduler request for these resources (how many nodes are required to run a given experiment, etc.).

The experiment is required to specify:

n_ranks it requires

n_gpus (if using GPUs)

If the experiment does not specify n_nodes, the modifier will compute the number of nodes to allocate to provide the n_ranks and/or n_gpus required for the experiment.

The system is required to specify:

sys_cores_per_node

sys_gpus_per_node (if it has GPUs)

sys_mem_per_node_GB

The modifier checks the resources requested by the experiment, computes the values for the unspecified variables, and checks that the request does not exceed the resources available on the system.

The resource allocation modifier is used by default in your experiment. However, it will only calculate values if you have not specified them yourself.

If you do not specify values, it will assign the default values as listed below.

Default Values For the Allocation Modifier
Variable	Default Value
n_nodes	(n_ranks / sys_cores_per_node) OR (n_gpus / sys_gpus_per_node) whichever is greater
n_ranks	(n_nodes * n_ranks_per_node) OR (n_gpus)
n_gpus	0
n_threads_per_proc	1

Hwloc: Hardware Map

The hwloc modifier enables using hwloc in Benchpark to record the hierarchical map of key computing elements on the given system, such as: NUMA memory nodes, shared caches, processor sockets, processor cores, and processor threads.

To use the hwloc modifier, add hwloc=on to the experiment init setup step. This modifier is disabled by default (hwloc=none).:

benchpark experiment init --dest=</path/to/experiment_root> --system=</path/to/system> <benchmark> caliper=<caliper_variant> hwloc=on

The hwloc modifier will output the hardware information in a flattened JSON file in the experiment directory.

If also running with the caliper modifier, hwloc information will be included in the Caliper metadata.