NEST benchmarks

NB! This repository is no longer updated!

We have moved our benchmarking efforts to GIN on G-node. For more information, contact [email protected].

To run the benchmarks you need to clone the github repository nest-benchmarks

This will create the folder nest-benchmarks, and it contains all JUBE files and sli scripts etc. needed to run the benchmarks. On the same level as nest-benchmarks you also need to create a folder called BenchWork, where NEST will be installed, and where the JUBE files will be generated.

First step

You might need to adjust the path for bench_home, bench_work, mesocircuit_home and MAM_home folders defined in nest-benchmarks/Benchmarks/jube_config/dir_config.xml.

If you for instance are running from the $SCRATCH folder, you should replace ${HOME} with $SCRATCH.

The benchmarks are run using the automatic benchmarking environment JUBE, so if you not already have this installed, you will need to do so.

Software Installation

Before running the Benchmarks, you need to install NEST, and you should also install jemalloc.

NEST

cd <PATH>/BenchWork
mkdir NEST
cd NEST
git clone https://github.com/nest/nest-simulator.git src

You need to clone intosrc. If you want a specific version of NEST, you need to adjust accordingly, you can find our build files here to see which build files we have already created.

Once the source code is in place, build and install NEST via JUBE:

jube run nest-benchmarks/Benchmarks/jube_build/build_nest_daint_strict.xml

NEST will be installed in BenchWork/NEST-daint-base_O3_strict.

NEST with Python

If you want to build and install NEST with Python, run

jube run nest-benchmarks/Benchmarks/jube_build/build_nest_py_daint_strict.xml

NEST with Boost

If you want to run NEST with Boost, you need to manually copy out lines 35, 38, 188,193, 194 and 195 in <PATH>/BenchWork/NEST/src/libnestutil/sort.h

Jemalloc

cd <PATH>/BenchWork
mkdir jemalloc
cd jemalloc
wget https://github.com/jemalloc/jemalloc/releases/download/5.0.1/jemalloc-5.0.1.tar.bz2
tar xvf jemalloc-5.0.1.tar.bz2

To build and install jemalloc, run

jube run nest-benchmarks/Benchmarks/jube_build/build_jemalloc_daint.xml

Running Benchmarks

The JUBE files for the benchmarks can be found in <PATH>/nest-benchmarks/Benchmarks/jube_bench/, while the benchmark scripts can be found in <PATH>/nest-benchmarks/Benchmarks/hpc_scipts/ or in <PATH>/nest-benchmarks/BenchModels/.

To run a benchmark, run

jube run nest-benchmarks/Benchmarks/jube_bench/<benchmark_file.xml>

You will get a job id.

These are the benchmarks we currently have, with corresponding JUBE files:

• HPC_benchmark

  • hpc_benchmark_daint_strict.xml

• HPC_benchmark with static synapse

  • hpc_benchmark_daint_strict.xml

    • where we change the parameter PLASTIC to false:

      ​ <parameter name="PLASTIC" type="string">false</parameter>

• HPC_benchmark with random delay

  • hpc_benchmark_daint_strict.xml

    • where we change the parameters D_MIN and D_MAX:

      ​ <parameter name="D_MIN" type="float">0.1</parameter>

      ​ <parameter name="D_MAX" type="float">50.</parameter>

• HPC benchmark split into many Connect calls

  • hpc_benchmark_daint_strict_split.xml
    • Either with <parameter name="NBLOCKS" type="int" mode="python">1000</parameter>
    • or with <parameter name="NBLOCKS" type="int" mode="python">1000*$NUMBER_OF_NODES</parameter>

• HPC benchmark with different connection rules

  • hpc_benchmark_daint_strict_rule.xml

    • use in, out, all, one, tot, bern orsym_bern in RULE parameter

      ​ <parameter name="RULE" type="string">in</parameter>

    • Probably need to use a lower basis_scale to get some of them to work, like basis_scale=5

• HPC benchmark with many synapses per neuron

  • hpc_benchmark_synapse_daint_strict.xml
  • needs to be run with basis_scale=44.4 in order to be able to compare to the population model.

• Population model

  • population_daint_strict.xml

• Multi-Area Model

  • multi-area-model_daint_strict.xml

• 4x4 Mesocircuit model

  • 4x4_mesocircuit_daint_strict.xml

• HPC Benchmark on fixed VPs, changing threads

  • hpc_benchmark_daint_strict.xml
    • with <parameter name="THREADS_PER_TASK" type="int">1,3,6,9,18,36</parameter>

Some benchmark information

HPC split into many Connect calls

This is the hpc_benchmark, but split into a lot ofConnect calls. We split the excitatory and inhibitory neurons into NBLOCKS populations each. For each of the excitatory and inhibitory blocks, we connect to 0.1*NBLOCKS populations.

With<parameter name="NBLOCKS" type="int" mode="python">1000</parameter>, we have a constant number of NBLOCKS = 1000, but when <parameter name="NBLOCKS" type="int" mode="python">1000*$NUMBER_OF_NODES</parameter>, NBLOCKS increase with the number of nodes, so we get more and more Connect calls.

HPC with many synapses per neuron

This is the hpc_benchmark, but with about 5000 connections per neuron.

Population model

The model has several populations (minimum 20 populations). Each population has 5000 neurons, and each neuron has 5000 connections. Each population connects to 20 other populations, which are chosen randomly. There are thus 250 connections per neuron and projection. The way we scale up is by adding more populations. We still connect to 20 other populations.

To run MAM:

You need to have installed NEST with Python in order to run the MAM benchmark.

jube run nest-benchmarks/Benchmarks/jube_build/build_nest_py_daint_strict.xml

You also need to download nested_dict and dicthash:

pip install nested_dict --user
pip install dicthash --user

On Piz-Daint we use the Python 3 module load cray-python/3.6.5.1.

To run 4x4:

To install the 4x4 mesocircuit model, run

module load cray-python/2.7.15.1
module load PyExtensions/2.7.15.1-CrayGNU-18.08
module load h5py/2.8.0-CrayGNU-18.08-python2-parallel
pip install NeuroTools

Then, go to nest-benchmarks/BenchModels/4x4mm2LFP/ and run

> python setup.py install --user

Specifications: All of the benchmarks are run with the following number of nodes, VPs, scales, threads unless otherwise specified:

Nodes	VPs	Scale - hpc	Scale - hpc_syn	Scale - hpc_rule	Scale - pop	Scale - MAM	Scale - 4x4	Threads
1	36	20	44.4	5	5	0.0625	0.0625	6
2	72	40	88.8	10	10	0.125	0.125	6
4	144	80	177.6	20	20	0.25	0.25	6
8	288	160	355.2	40	40	0.5	0.5	6
16	576	320	710.4	80	80	1.0	1.0	6
32	1152	640	1420.8	160	160	2.0	2.0	6

Analyze Benchmarks

When the benchmark is finished running, tell JUBE to analyze the results

jube analyse BenchWork/<jube_directory>/<nest_version>/ -i id

Then get the results

jube result BenchWork/<jube_directory>/<nest_version>/ -i id

If you want to save the results in a csv-file, do

jube result BenchWork/<jube_directory>/<nest_version>/ -i id > <path-to-directory-you-want>/result-name.csv

result-name.csv can be an existing file, or new one. If it already exists, this will overwrite the content in the file.

Visualization

You can look at nest-benchmarks/benchmark_plots/benchmark_plots.ipynb and use result-name.csv to visualize the results.