ReFrame Test Library (experimental)

This is a collection of generic tests that you can either run out-of-the-box by specializing them for your system using the -S option or create your site-specific tests by building upon them.

Data Analytics

class hpctestlib.data_analytics.spark.spark_checks.compute_pi_check(*args, **kwargs)

Bases: RunOnlyRegressionTest

Test Apache Spark by computing PI.

Apache Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Java, Scala, Python and R, and an optimized engine that supports general execution graphs. It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for incremental computation and stream processing (see spark.apache.org).

This test checks that Spark is functioning correctly. To do this, it is necessary to define the tolerance of acceptable deviation. The tolerance is used to check that the computations are executed correctly, by comparing the value of pi calculated to the one obtained from the math library. The default assumption is that Spark is already installed on the system under test.

assert_pi_readout()

Assert that the obtained pi value meets the specified tolerances.

exec_cores = 1

The number of cores per each Spark executor

Type:

int

Required:

No

Default:

1

executor_memory

Amount of memory to use per executor process, following the JVM memory strings convention, i.e a number with a size unit suffix (“k”, “m”, “g” or “t”) (e.g. 512m, 2g)

Type:

str

Required:

Yes

num_workers = 1

The number of Spark workers per node

Type:

int

Required:

No

Default:

1

spark_local_dirs = /tmp

The local directories used by Spark

Type:

str

Required:

No

Default:

‘/tmp’

spark_prefix

The Spark installation prefix path

Type:

str

Required:

Yes

tolerance = 0.01

The absolute tolerance of the computed value of PI

Type:

float

Required:

No

Default:

0.01

variant = <reframe.core.parameters.TestParam object>

Parameter encoding the variant of the test.

Type:

str

Values:

['spark', 'pyspark']

Interactive Computing

class hpctestlib.interactive.jupyter.ipcmagic.ipcmagic_check(*args, **kwargs)

Bases: RunOnlyRegressionTest

Test ipcmagic via a distributed TensorFlow training with ipyparallel.

ipcmagic is a Python package and collection of CLI scripts for controlling clusters for Jupyter. For more information, please have a look here.

This test checks the ipcmagic performance. To do this, a single-layer neural network is trained against a noisy linear function. The parameters of the fitted linear function are returned in the end along with the resulting loss function. The default assumption is that ipcmagic is already installed on the system under test.

assert_successful_execution()

Checks that the program is running on 2 different nodes (hostnames are different), that IPCMagic is configured and returns the correct end-of-program message (returns the slope parameter in the end).

Machine Learning

class hpctestlib.ml.tensorflow.horovod.tensorflow_cnn_check(*args, **kwargs)

Bases: RunOnlyRegressionTest

Run a synthetic CNN benchmark with TensorFlow2 and Horovod.

TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications. For more information, refer to https://www.tensorflow.org/.

Horovod is a distributed deep learning training framework for TensorFlow, Keras, PyTorch, and Apache MXNet. The goal of Horovod is to make distributed deep learning fast and easy to use. For more information refer to https://github.com/horovod/horovod.

This test runs the Horovod tensorflow2_synthentic_benchmark.py example, checks its sanity and extracts the GPU performance.

batch_size = 32

The size of the batch used during the learning of models.

Type:

int

Default:

32

benchmark_version = v0.21.0

The version of Horovod to use.

Type:

str

Default:

'v0.21.0'

model = InceptionV3

The name of the model to use for this benchmark.

Type:

str

Default:

'InceptionV3'

num_batches_per_iter = 5

The number of batches per iteration.

Type:

int

Default:

5

num_iters = 5

The number of iterations.

Type:

int

Default:

5

num_warmup_batches = 5

The number of warmup batches

Type:

int

Default:

5

throughput_iteration()

The average GPU throughput per iteration in images/s.

throughput_total()

The total GPU throughput of the benchmark in images/s.

class hpctestlib.ml.pytorch.horovod.pytorch_cnn_check(*args, **kwargs)

Bases: RunOnlyRegressionTest

Run a synthetic CNN benchmark with PyTorch and Horovod.

PyTorch is a Python package that provides tensor computation like NumPy with strong GPU acceleration and deep neural networks built on a tape-based autograd system. For more information, refer to https://pytorch.org/.

Horovod is a distributed deep learning training framework for TensorFlow, Keras, PyTorch, and Apache MXNet. The goal of Horovod is to make distributed deep learning fast and easy to use. For more information refer to https://github.com/horovod/horovod.

This test runs the Horovod pytorch_synthentic_benchmark.py example, checks its sanity and extracts the GPU performance.

batch_size = 64

The size of the batch used during the learning of models.

Type:

int

Default:

64

benchmark_version = v0.21.0

The version of Horovod to use.

Type:

str

Default:

'v0.21.0'

model = inception_v3

The name of the model to use for this benchmark.

Type:

str

Default:

'inception_v3'

num_batches_per_iter = 5

The number of batches per iteration.

Type:

int

Default:

5

num_iters = 5

The number of iterations.

Type:

int

Default:

5

num_warmup_batches = 5

The number of warmup batches

Type:

int

Default:

5

throughput_iteration()

The average GPU throughput per iteration in images/s.

throughput_total()

The total GPU throughput of the benchmark in images/s.

Microbenchmarks

OSU microbenchmarks

There are two final parameterized tests that represent the various OSU benchmarks:

• The osu_run test that runs the benchmarks only. This assumes that the OSU microbenchmarks are installed and available.

• The osu_build_run test that builds and runs the benchmarks. This test uses two fixtures in total: one to build the tests and one to fetch them.

Depending on your setup you can select the most appropriate final test. The benchmarks define various variables with a reasonable default value that affect the execution of the benchmark. For collective communication benchmarks, setting the num_tasks is required. All tests set num_tasks_per_node to 1 by default.

Examples

Run the run-only version of the point to point bandwidth benchmark:

reframe -n 'osu_run.*benchmark_info=mpi.pt2pt.osu_bw' -S modules=my-osu-benchmarks -S valid_systems=mysystem -S valid_prog_environs=myenv -l

Build and run the CUDA-aware version of the allreduce benchmark.

reframe -n 'osu_build_run.*benchmark_info=mpi.collective.osu_allreduce.*build_type=cuda' -S device_buffers=cuda -S num_tasks=16 -S valid_systems=sys:part -S valid_prog_environs=myenv -l

class hpctestlib.microbenchmarks.mpi.osu.build_osu_benchmarks(*args, **kwargs)

Bases: CompileOnlyRegressionTest

Fixture for building the OSU benchmarks

build_type = <reframe.core.parameters.TestParam object>

Build variant parameter.

Type:

str

Values:

'cpu', 'cuda', 'rocm', 'openacc'

osu_benchmarks = <reframe.core.fixtures.TestFixture object>

The fixture object that retrieves the benchmarks

Type:

fetch_osu_benchmarks

Scope:

session

class hpctestlib.microbenchmarks.mpi.osu.fetch_osu_benchmarks(*args, **kwargs)

Bases: RunOnlyRegressionTest

Fixture for fetching the OSU benchmarks.

version = 5.9

The version of the benchmarks to fetch.

Type:

str

Default:

'5.9'

class hpctestlib.microbenchmarks.mpi.osu.osu_benchmark(*args, **kwargs)

Bases: RunOnlyRegressionTest

OSU benchmark test base class.

benchmark_info = <reframe.core.parameters.TestParam object>

Parameter indicating the available benchmark to execute.

Type:

2-element tuple containing the benchmark name and whether latency or bandwidth is to be measured.

Values:

mpi.collective.osu_alltoall, mpi.collective.osu_allreduce, mpi.pt2pt.osu_bw, mpi.pt2pt.osu_latency

device_buffers = cpu

Device buffers.

Use accelerator device buffers. Valid values are cpu, cuda, openacc or rocm.

Type:

str

Default:

'cpu'

message_size

Maximum message size.

Both the performance and the sanity checks will be done for this message size.

This value is set to 8 for latency benchmarks and to 4194304 for bandwidth benchmarks.

Type:

int

num_iters = 1000

Number of iterations.

This value is passed to the excutable through the -i option.

Type:

int

Default:

1000

num_tasks

Number of tasks to use.

This variable is required. It is set to 2 for point to point benchmarks, but it is undefined for collective benchmarks

Required:

Yes

num_warmup_iters = 10

Number of warmup iterations.

This value is passed to the excutable through the -x option.

Type:

int

Default:

10

class hpctestlib.microbenchmarks.mpi.osu.osu_build_run(*args, **kwargs)

Bases: osu_benchmark

OSU benchmark test (build and run)

osu_binaries = <reframe.core.fixtures.TestFixture object>

The fixture object that builds the OSU binaries

Type:

build_osu_benchmarks

Scope:

environment

class hpctestlib.microbenchmarks.mpi.osu.osu_run(*args, **kwargs)

Bases: osu_benchmark

Run-only OSU benchmark test

GPU benchmarks

class hpctestlib.microbenchmarks.gpu.gpu_burn.gpu_burn_build(*args, **kwargs)

Bases: CompileOnlyRegressionTest

Fixture for building the GPU burn benchmark.

Summary

Variables	Parameters	Fixtures
gpu_arch gpu_build	None	None

gpu_arch = None

Set the GPU architecture.

This variable will be passed to the compiler to generate the arch-specific code.

Type:

str or None

Default:

None

gpu_build = None

Set the build option to either 'cuda' or 'hip'.

Type:

str

Default:

'cuda'

class hpctestlib.microbenchmarks.gpu.gpu_burn.gpu_burn_check(*args, **kwargs)

Bases: RunOnlyRegressionTest

GPU burn benchmark.

This benchmark runs continuously GEMM, either single or double precision, on a selected set of GPUs on the node where the benchmark runs.

The floating point precision of the computations, the duration of the benchmark as well as the list of GPU devices that the benchmark will run on can be controlled through test variables.

This benchmark tries to build the benchmark code through the gpu_burn_build fixture.

This benchmark sets the num_gpus_per_node test attribute, if not already set, based on the number of devices with type == 'gpu' defined in the corresponding partition configuration. Similarly, this benchmark will use the arch device configuration attribute to set the gpu_arch variable, if this is not already set by the user.

Summary

Variables	Parameters	Metrics	Fixtures	System features	Environment features
use_dp duration devices	None	gpu_perf_min gpu_temp_max	gpu_burn_build [E]	+gpu	+cuda OR +hip

devices = []

List of device IDs to run the benchmark on.

If empty, the benchmark will run on all the available devices.

Type:

List[int]

Default:

[]

duration = 10

Duration of the benchmark in seconds.

Type:

int

Default:

10

gpu_perf_min()

Lowest performance recorded among all the selected devices.

gpu_temp_max()

Maximum temperature recorded among all the selected devices.

use_dp = True

Use double-precision arithmetic when running the benchmark.

Type:

bool

Default:

True

Python

class hpctestlib.python.numpy.numpy_ops.numpy_ops_check(*args, **kwargs)

Bases: RunOnlyRegressionTest

NumPy basic operations test.

NumPy is the fundamental package for scientific computing in Python. It provides a multidimensional array object, various derived objects (such as masked arrays and matrices), and an assortment of routines for fast operations on arrays, including mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms, basic linear algebra, basic statistical operations, random simulation and much more.

This test test performs some fundamental NumPy linear algebra operations (matrix product, SVD, Cholesky decomposition, eigendecomposition, and inverse matrix calculation) and users the execution time as a performance metric. The default assumption is that NumPy is already installed on the currest system.

time_cholesky()

Time of the cholesky kernel in seconds.

time_dot()

Time of the dot kernel in seconds.

time_eigendec()

Time of the eigendec kernel in seconds.

time_inv()

Time of the inv kernel in seconds.

time_svd()

Time of the svd kernel in seconds.

Scientific Applications

class hpctestlib.sciapps.amber.nve.amber_nve_check(*args, **kwargs)

Bases: RunOnlyRegressionTest

Amber NVE test.

Amber is a suite of biomolecular simulation programs. It began in the late 1970’s, and is maintained by an active development community.

This test is parametrized over the benchmark type (see benchmark_info) and the variant of the code (see variant). Each test instance executes the benchmark, validates numerically its output and extracts and reports a performance metric.

assert_energy_readout()

Assert that the obtained energy meets the required tolerance.

property bench_name

The benchmark name.

Type:

str

benchmark_info = <reframe.core.parameters.TestParam object>

Parameter pack encoding the benchmark information.

The first element of the tuple refers to the benchmark name, the second is the energy reference and the third is the tolerance threshold.

Type:

Tuple[str, float, float]

Values:

[
    ('Cellulose_production_NVE', -443246.0, 5.0E-05),
    ('FactorIX_production_NVE', -234188.0, 1.0E-04),
    ('JAC_production_NVE_4fs', -44810.0, 1.0E-03),
    ('JAC_production_NVE', -58138.0, 5.0E-04)
]

property energy_ref

The energy reference value for this benchmark.

Type:

str

property energy_tol

The energy tolerance value for this benchmark.

Type:

str

input_file

The input file to use.

This is set to mdin.CPU or mdin.GPU depending on the test variant during initialization.

Type:

str

Required:

Yes

num_tasks

See num_tasks.

The mpi variant of the test requires num_tasks > 1.

Required:

Yes

output_file = amber.out

The output file to pass to the Amber executable.

Type:

str

Required:

No

Default:

'amber.out'

perf()

The performance of the benchmark expressed in ns/day.

class hpctestlib.sciapps.gromacs.benchmarks.gromacs_check(*args, **kwargs)

Bases: RunOnlyRegressionTest

GROMACS benchmark test.

GROMACS is a versatile package to perform molecular dynamics, i.e. simulate the Newtonian equations of motion for systems with hundreds to millions of particles.

The benchmarks consist on a set of different inputs files that vary in the number of atoms and can be found in the following repository, which is also versioned: https://github.com/victorusu/GROMACS_Benchmark_Suite/.

Each test instance validates numerically its output and extracts and reports a performance metric.

assert_energy_readout()

Assert that the obtained energy meets the benchmark tolerances.

property bench_name

The benchmark name.

Type:

str

benchmark_info = <reframe.core.parameters.TestParam object>

Parameter pack encoding the benchmark information.

The first element of the tuple refers to the benchmark name, the second is the energy reference and the third is the tolerance threshold.

Type:

Tuple[str, float, float]

Values:

benchmark_version = 1.0.0

The version of the benchmark suite to use.

Type:

str

Default:

'1.0.0'

property energy_ref

The energy reference value for this benchmark.

Type:

str

property energy_tol

The energy tolerance value for this benchmark.

Type:

str

nb_impl = <reframe.core.parameters.TestParam object>

Parameter encoding the implementation of the non-bonded calculations

Type:

str

Values:

['cpu', 'gpu']

ReFrame benchmark for QuantumESPRESSO

class hpctestlib.sciapps.qespresso.benchmarks.QEspressoPWCheck(*args, **kwargs)

Bases: RunOnlyRegressionTest

QuantumESPRESSO benchmark test.

QuantumESPRESSO is an integrated suite of Open-Source computer codes for electronic-structure calculations and materials modeling at the nanoscale.

The benchmarks consist of one input file templated inside the code and a pseudo-potential file that is downloaded from the official repository.

This tests aims at measuring the scalability of the pw.x executable, in particular the FFT and diagonalization algorithms, by running a simple silicon calculation with high ecut (increases size of FFTs) and nbnd (increases size of matrices to diagonalize) values.

assert_job_finished()

Check if the job finished successfully

static convert_timings(timing: str) → float

Convert timings to seconds

ecut = <reframe.core.parameters.TestParam object>

Parametert to tests the performance of the FFTW algorithm, higher ecut implicates more FFTs

Type:

int

Values:

[50, 150]

extract_report_time(name: str = None, kind: str = None) → float

Extract timings from pw.x stdout

Args:

name (str, optional): Name of the timing to extract.

Defaults to None.

kind (str, optional): Kind (‘cpu’ or ‘wall) of timing to extract.

Defaults to None.

Raises:

ValueError: If the kind is not ‘cpu’ or ‘wall’

Returns:

float: The timing in seconds

static extractsingle_or_val(*args, on_except_value: str = '0s') → str

Wrap extractsingle_or_val to return a default value if the regex is not found.

Returns:

str: The value of the regular expression

input_name: str = Si.scf.in

The name of the input file used.

Type:

str

Default:

'Si.scf.in'

nbnd = <reframe.core.parameters.TestParam object>

Parameter to Tests the performance of the diagonalization algorithm, higher nbnd implicates bigger matrices

Type:

int

Values:

[10, 200]

pp_name: str = Si.pbe-n-kjpaw_psl.1.0.0.UPF

The pseudo-potential file to be used check https://www.quantum-espresso.org/pseudopotentials/ for more info

Type:

str

Default:

'Si.pbe-n-kjpaw_psl.1.0.0.UPF'

prepare_test()

Hook to the set the downloading of the pseudo-potentials

set_perf_variables()

Build a dictionary of performance variables

write_input()

Write the input file for the calculation

System

class hpctestlib.system.fs.mnt_opts.filesystem_options_check(*args, **kwargs)

Bases: RunOnlyRegressionTest

filesystem mount options check

Check if the mounted filesystems have been configured appropriately based on their type

fail_unknown_fs = False

Fail if the test finds a filesystem type that is not in the

reference dictionary

Type:

Bool.

Value:

False

fs_ref_opts

Reference mount options

Type:

Dict[str, str]. The key should be the file system type. and the value should be a string with mount options. E.g., {‘xfs: ‘nosuid,logbsize=32k’}

print_test_variables_to_output()

Write the reference mount point options used by the test at the time of execution.