Simulating decision making data and parameter recovery
Running the simulation
To run the simulation, edit run_simulation.py so that you request a Pool size that's suitable for your machine.
Then do,
python run_simulation.py
There are a bunch of warnings. If you are happy to ignore them, run it with
python -W ignore run_simulation.py
Setting up the Conda environment on ShARC
Before we can run hddm on ShARC, you need to install the conda environment in your home directory.
- Log in and start a
qrshxsession. - Load the conda module
module load apps/python/conda
- Create the base hddm environment
conda create -n hddm python=3.4
You will be faced by a blank screen for quite a while. Don't worry, it hasn't crashed (probably!).
- Source the new environment and install gddm
source activate hddm
conda install -c pymc hddm
Running the simulation on ShARC
To run the simulation currently on GitHub
git clone https://github.com/mikecroucher/ddm_sims
cd ddm_sims
qsub sharc_submit.sh
The level of parallelisation is controlled in two places. You need to ensure that they are set to the same value.
To use 16 cores
In sharc_submit.sh:
#$ -pe smp 16
In run_simulation.py
pool = Pool(16)
The parallelisation scheme currently used is limited to the number of cores available on a single node. This is currently 16. 32 core nodes are available but only to people who have contributed to the RSE group's premium queue
We could explore additional schemes for better scaling.
Parallelisation timings (small job)
With the following parameters
n_experiments = 50 # Number of simulated experiments - make this arbitrary large for final run
n_subjects = [10,20,30,100] # n_participants in each experiment
stim_A = 0.2 # Stimulus A - Drift Rate = 0.2
stim_B = 0.3 # Stimulus B - Drift Rate = 0.6
intersubj_drift_var=0.1 # std
n_samples = 100 #for HDDM fitting, put this to 5000 for final run
trial_name = 'ppt_test' # Specfies what each trial is running - e.g. altered number of participants
trials = 20 # trial per participants
4 core Mid 2014 Macbook Pro There seems to be very little to gain from oversubscribing the cores.
- 1 process - 1790 seconds
- 4 processes - 584 seconds
- 8 processes - 567 seconds (3.1x speedup)
16 core node on ShARC
These are the standard nodes -- available to everyone.
- 16 processes 208.452 seconds
32 core node on ShARC
Note that this is only available to people who have contributed to the RSE group's premium queue. They are more powerful than anything else on ShARC
- 32 processes - 122.661 seconds (14.7x faster than the Mac using 1 core)
Parallelisation timings (large job)
n_experiments = 100 # Number of simulated experiments - make this arbitrary large for final run
n_subjects = [10,20,30,100] # n_participants in each experiment
stim_A = 0.2 # Stimulus A - Drift Rate = 0.2
stim_B = 0.3 # Stimulus B - Drift Rate = 0.6
intersubj_drift_var=0.1 # std
n_samples = 5000 #for HDDM fitting, put this to 5000 for final run
trial_name = 'ppt_test' # Specfies what each trial is running - e.g. altered number of participants
trials = 20 # trial per participants
- 32 processes - 6570 seconds