Distributed processing and cluster configuration¶
One of the main reasons for existence of the pi2 system is its ability to do user-transparent distributed processing. Let us look at a simple example in Python code:
# Initialization from pi2py2 import * pi = Pi2() # Read two images img1 = pi.read('./first_image') img2 = pi.read('./second_image') # Add them together pi.add(img1, img2) # Save pi.writeraw(img1, './added_image')
This code initializes the pi2 system, reads two images from disk (in any supported format), adds
img1 and finally saves the result to disk.
If the images do not fit into the RAM of the computer, we have a problem. The input images must be read in smaller pieces, operations performed on the pieces, and the result must be written into the output file at the correct location. Doing all this is a bit complicated, particularly if the processing is something more complicated than simple addition. Luckily, pi2 is able to do all this with only minimal changes to the code:
# Initialization from pi2py2 import * pi = Pi2() # Enable distributed processing pi.distribute(Distributor.LOCAL) # Read two images img1 = pi.read('./first_image') img2 = pi.read('./second_image') # Add them together pi.add(img1, img2) # Save pi.writeraw(img1, './added_image')
We only need to add the line
pi.distribute(...) in order to enable the distributed computing mode.
The argument specifies what kind of distribution strategy to use.
Currently, supported strategies are
The local mode divides the input and output images into smaller pieces and processes the pieces one-by-one on your local computer. This mode can be used to process datasets that do not fit into the RAM of your computer.
The Slurm mode assumes that you are running on a computer cluster using the Slurm Workload Manager. It divides the datasets similarly to the local mode, but submits processing of each piece to the cluster as a Slurm job. This way all the cluster nodes available to you can be benefited from, and processing of very large (even terabyte-scale) images is pretty fast.
The distributed operating mode is supported by most of the pi2 commands. See the Command reference for details.
To fully benefit from the distributed mode, some settings must be tuned as detailed in the following sections.
Configuration for local distribution mode¶
Configuration for local distribution mode are found in file local_config.txt found in the same folder than the pi2 executable.
In the configuration file, lines starting with
;-character are comments.
In the default configuration file the comments are used to describe the different settings.
The most important setting is
max_memory that gives the approximate amount of memory (in megabytes) that the pi2 system may use at once.
If set to zero, pi2 uses 85 % of total RAM in the computer.
For descriptions of the other settings, please refer to the comments in the default configuration file.
For quick testing, the
maxmemory parameter can also be set using the maxmemory command, but changes made with the command are not saved into the configuration files.
Configuration for SLURM cluster¶
Slurm cluster configuration is located in file slurm_config.txt found in the same folder than the pi2 executable. The file is similar to the local_config.txt (see above), but it has more parameters available.
The pi2 system divides the jobs into three categories: fast, normal, and slow.
extra_args_* settings can be used to submit the jobs into different partitions.
This is useful as partitions for fast jobs usually have shorter queueing time.
max_memory setting (and
maxmemory command) works the same than in local processing, but now it should be set to the amount of usable RAM in a compute node.
If set to zero, the system finds out the node with the smallest amount of memory using
sinfo --Node --Format=memory, and sets
max_memory to 90 % of that.
In very large clusters the
sinfo command may be slow, so you might want to set the
max_memory parameter manually.
If you encounter out-of-memory errors or jobs crash without reason, try decreasing the
When a job fails, the pi2 system will try to re-submit it a few times. The maximum number of re-submissions is given by the
For more thorough descriptions of the parameters please refer to comments in the default configuration file.