- Single NAMD Job
- N-Sequential jobs
- M-Parallel, N-Sequential jobs
- Getting Help
- [x] Become familiar with the Pegasus workflow management system.
- [x] Use it to run a large scale NAMD simulation task.
The Pegasus Workflow Management System
Pegasus, a workflow management framework developed at the Information Sciences Institute at the University of Southern California, can handle millions of computational tasks and takes care of managing input/output files for you. Pegasus enables scientists to construct workflows in abstract terms without worrying about the details of the underlying execution environment or the particulars of the low-level specifications required by the middleware. Some of the advantages of using Pegasus includes:
Performance - The Pegasus mapper can reorder, group, and prioritize tasks in order to increase the overall workflow performance.
Scalability - Pegasus can easily scale both the size of the workflow, and the resources that the workflow is distributed over. Pegasus runs workflows ranging from just a few computational tasks up to 1 million. The number of resources involved in executing a workflow can scale as needed without any impediments to performance.
Portability / Reuse - User created workflows can easily be run in different environments without alteration. Pegasus currently runs workflows on top of Condor, Grid infrastrucutures such as Open Science Grid and TeraGrid, Amazon EC2, Nimbus, and many campus clusters. The same workflow can run on a single system or across a heterogeneous set of resources.
Data Management - Pegasus handles replica selection, data transfers and output registrations in data catalogs. These tasks are added to a workflow as auxiliary jobs by the Pegasus planner.
Reliability - Jobs and data transfers are automatically retried in case of failures. Debugging tools such as pegasus-analyzer helps the user to debug the workflow in case of non-recoverable failures.
Provenance - By default, all jobs in Pegasus are launched via the kickstart process that captures runtime provenance of the job and helps in debugging. The provenance data is collected in a database, and the data can be summaries with tools such as pegasus-statistics, pegasus-plots, or directly with SQL queries.
Error Recovery - When errors occur, Pegasus tries to recover when possible by retrying tasks, by retrying the entire workflow, by providing workflow-level checkpointing, by re-mapping portions of the workflow, by trying alternative data sources for staging data, and, when all else fails, by providing a rescue workflow containing a description of only the work that remains to be done.
Single NAMD Job
The necessary files are available to the user by invoking the tutorial command.
$ tutorial ### Without an argument, it shows the list of available tutorials. $ tutorial pegasus-namd ### The files to run the pegasus-namd tutorial are created under the directory tutorial-pegasus-namd $ cd tutorial-pegasus-namd/Single ### First, we will focus on running single NAMD job with pegasus workflow management
Figure 1. The actual workflow of executing single job is transformed into a set of jobs in Pegasus workflow. Such a transformation is useful to keep track of input and output data. In particular, when we have to deal with lot of jobs.
Now we will take a closer look at the files to understand how to create the Pegasus workflow for single job.
Input and output files
There are several files and directories under the path
tutorial-pegasus-namd/Single. Some files are required to run NAMD simulations and other files are related to pegasus workflow management. The files required by NAMD are under the directories
inputs/ # Directory contains the input configuration file for NAMD ExeFiles/ # Directory contains Bash script file that executes the NAMD with relevant arguments paramdirs/ # Directory contains the structure, psf and topology files
The following files are related to Pegasus workflow management:
pegasusrc # The configuration file for pegasus dax-generator-singleJob.py # Python script generates dax.xml file sites-generator.bash # Bash script generates sites.xml file submit.bash # Bash script submits the pegasus workflow.
pegasusrc contains the pegasus configuration information. We can simply keep this file in the current working directory without worrying much about the details (if you would like to know the details, please visit the Pegasus home page). The files
sites.xml contain the information about the work flow and data management.
Let us pay attention to a few parts of the
submit.bash script to understand about submitting the workflow. Open the file
submit.bash and take a look:
... line 9 ./dax-generator-singleJob.py ### Execution of "dax-generator-singleJob.py" script. Generates dax.xml. ... line 14 ./sites-generator.bash ### Execution of "sites-generator.bash" script. Generates sites.xml. ... line 17 pegasus-plan \ ### Executes the pegasus-plan with the following arguments line 18 --conf pegasusrc \ ### pegasus configuration file line 19 --sites condorpool \ ### jobs are executed in condorpool line 20 --dir $PWD/workflows \ ### The path of the workflow directory line 21 --output-site local \ ### Outputs are directed to the local site. line 22 --dax dax.xml \ ### Name of the dax file line 23 --submit ### Type of action is submit
The purpose of
sites-generator.bash script is to generate the
sites.xml file. There are several lines declared in the
sites-generator.bash script. We need to understand the lines defining the
... line 4 cat >sites.xml <<EOF ### creates the file "sites.xml" and appends the following lines. ... line 11 <file-server protocol="file" url="file://" mount-point="$PWD/scratch"/> ### Define the path of scratch directory line 12 <internal-mount-point mount-point="$PWD/scratch"/> ### Define the path of scratch directory ... line 17 <file-server protocol="file" url="file://" mount-point="$PWD/outputs"/> ### Define the path of output directory line 18 <internal-mount-point mount-point="$PWD/outputs"/> ### Define the path of output directory ... line 32 EOF ### End of sites.xml file The files "submit.bash" and "sites-generator.bash" will not change very much for a new workflow. We need to edit these two files, when we change the name of the dax-generator and/or the path of outputs, scratch and workflows.
dax.xml contains the workflow information, including the description about the jobs and required input files. We could manually write the
dax.xml file but it is not very pleasant for the human eye to deal with XML format. Here,
dax.xml is generated via the Python script
dax-generator-singleJob.py. Take a look at the Python script. It is self explanatory with lots of comments. If you have difficulty to understand the script, please feel free to send us an email. Here we point out a few interesting lines:
... line 8 dax = ADAG("namd-singleJob") ### Name of dax. You can change any interesting name you like. ... line 11 ... ### Defines the directory paths of base, exe, inputs and param directories line 14 ... line 18 ... ### Add the executable "namd_exe.bash" and its path to the dax.xml file line 21 ... line 25 ... ### Loops over the files in the paramdirs. Add the param files and their path to the dax.xml file line 28 ... line 31 ... ### These lines describe the namd conig file "ubq_gbis_eq.conf" and its location. The information about config file is included in dax.xml file in way similar to param files were included. line 35 ... line 38 ### A job "namdEq_job" is added in dax.xml file. ... ### Define the job, input and output files. Pegasus would transfer the INPUT files to the remote worker nodes before job execution. Pegasus would transfer the OUTPUT files from the remote worker nodes after job execution. line 47 ... line 50 ... ### Although the path of the param files are defined in the lines 25-28, so far they are not defined as input files for the executable. Here, the param files are defined as input files so that pegasus transfer these files to the remote machine. line 53
Job submission and status
To submit the job:
### To submit the job $ ./submit.bash
To check the status of the submitted job:
$ pegasus-status ### or you can also check with the condor_q command $ condor_q username ### username is your login ID
Pegasus creates the following directories:
scratch/ ### Contains all the files (including input, parameter and execution) required to run the job are copied in this directory. workflows/ ### Contains the workflow files including DAGMan, data transfer scripts and condor job files. outputs/ ### Where the NAMD output files are stored at the end of each job.
The path of the scratch, workflows and outputs directories are declared in the
submit.bash script at lines 19, 20, 25,26 and 47.
Under the directory
tutorial-pegasus-namd/Exercises/SingleEx1 you will see relevant files to run the single NAMD job with Pegasus. However, you need to change few things to run submit the job. The errors are associated with the definition of names of
dax-generator and NAMD input files. You have to correct these two file names in the submit
submit.bash and in the dax-generator script.
Under the directory
tutorial-pegasus-namd/Exercises/SingleEx2 you will see relevant files. In this exercise, you have to specify the correct path for the scratch, output and workflow directories. All these information are included in the
The current workflow of N sequential jobs demonstrates the ability to complete large scale molecular dynamics simulations with Pegasus. We break the long-time scale simulation into several short-time scale simulations. The short simulations are performed in a sequence and then the results are combined to achieve the the long-time scale simulation. In this example, we will learn how to run N sequential NAMD jobs.
Here, the NAMD jobs are executed one-by-one. In these sequential executions, the restart files are utilized. A NAMD job generates the restart files that are necessary to start the next job. To run N-sequential NAMD jobs, we need N input files. We have to specify in the input file that the restart files from each simulation are available to the next job. So our first step is to generate N input files that are suitable to run N-sequential jobs.
Figure 2. The workflow to run a linear sequence of jobs. The blue circles represent jobs and the arrows represent the direction of data flow. This means data from J1 is required to start the job J2, the data from J2 is required to start the job J3 and so on.
Generating N-sequential input files
We use a script to do the task of generating N input files to run the sequential molecular dynamics simulations.
$ cd tutorial-pegasus-namd/N-Sequential # Directory of required files to run N-sequential MD jobs $ cd InputGen # Input generation script are available in this directory $ ./namd_gen_pegasus_input.bash 10 # Generates 10 configuration files for NAMD under the directory tutorial-pegasus-namd/N-Sequential/inputs
Basically, the script generates input files from a reference template. In these input files, the name of the restart files are specified in the correct order such that the input files are suitable for sequential execution of NAMD jobs. We have to change our
dax.xml via the DAX generator script to account that there are N-sequential jobs. Lets find the DAX generator script:
$ cd tutorial-pegasus-namd/N-Sequential # Main directory $ ls dax*.py dax-generator-namdEq-sequential.py # This is the dax generator file.
Now let's analyze the DAX generator script to find the primary differences between running N-sequential jobs vs running a single job. Open the file
... line 33 for i in range (0,10): # We loop over the number of input files. Note that if there 20 jobs the range is 20 in the loop. ... line 66 if job_count > 0: # We check the number of jobs to define the job dependencies. For each input file, we create a NAMD job. line 67 dax.addDependency(Dependency(parent=namdEq_jobOld, child=namdEq_job)) # The PARENT-CHILD relation is defined here. ...
We see it is easy to take the single job script and build the script for N-sequential jobs. In fact, we could take any DAX generator script and modify it for a new workflow. This is because the abstraction layer provided by Pegasus is a great strength in re-using the workflow or in modifying a workflow to fit closely related computational tasks. If we do not change the scratch and output directories, there is no need to change the
sites-generator.bash script. Next, we will work on the
Job submission and status
Since the DAX generator is
dax-generator-namdEq-sequential.py, we should have the file specified in the
submit.bash script to generate the
dax.xml file. Edit
submit.bash as follows:
... line 9 ./dax-generator-namdEq-sequential.py ### The dax-generator script is executed and it creates dax.xml file. ...
As mentioned before, we can submit and check the status of the job as follows:
$ ./submit.bash # submits the job $ pegasus-status # check the job status $ condor_q username # also checks job status. username is your login ID
Go to the directory
tutorial-pegasus-namd/Exercises/NSeqEx3. All the input files related to run 1,000 sequential NAMD jobs are in the file
inputsN1000.tar.gz. The input files were already generated to save time. Uncompress the files by running the command
tar -xvzf inputsN1000.tar.gz that will create a directory
inputs/ containing all the NAMD input files. Since the DAX generator Python script needs to know about these 1,000 input files, include this information in the dax-generator.
M-Parallel, N-Sequential jobs
We consider the case of running large molecular dynamics simulations of a protein for multiple temperatures. The solution is to generate M parallel simulations corresponding to the different temperatures. Each one of the parallel simulations contains N-sequential jobs. We can take the N-sequential workflow as template and modify the workflow to fit the M-parallel, N-sequential simulations.
Figure 3. The NAMD simulation is performed for M temperatures: T1, T2, ....TM. For each temperature there are N-sequential jobs.
Generating M*N input files
We use a script to do the task of generating M*N input files for M-parallel, N-Sequential molecular dynamics simulation.
$ cd tutorial-pegasus-namd/M_times_NSeq ### Directory of required files to run M-parallel, N-sequential MD jobs $ cd InputGen ### the input template and input generation script are available in this directory $ ./namd_gen_pegasus_input.bash 12 20 ### This would generate 12 independent MD simulations, each of 20 sequential config fils under the directory inputs
namd_gen_pegasus_input.bash generates M parallel, each of N sequential NAMD inputs. A given a parallel job containing N-sequential jobs will be carried out with a particular temperature that was generated from a random process. We have to change our
dax.xml via the DAX generator script to account that there are N-sequential jobs. Here is the DAX generator file:
$ cd tutorial-pegasus-namd/M_times_NSeq ### Main directory $ ls dax*.py dax-generator-namdEq-MtimesNSeq.py ### This is the dax generator file.
Let us analyze the DAX generator script to find the primary differences between running M-parallel, N-sequential jobs and running N-sequential jobs.
... line 8 Mjobs = 16 ### Defines the number of M-parallel jobs (for the current example, this number is 12) line 9 Njobs = 24 ### Defines the number of N-Sequential jobs (for the current example, this number is 24) ... line 36 for j in range(1,Mjobs+1): ### The loop for M-parallel jobs ...
The DAX generator for M-parallel, N-sequential jobs is adapted from the dax-generator script to run N-sequential jobs by adding an additional loop. Since the path of scratch and output directories are defined from the relative path of the working directory, there is no need to change the
sites-generator.script. Next, we will work on the
Job submission and status
Since the dax-generator is
dax-generator-namdEq-sequential.py, we should have the file called in the submit script to generate the
... line 9 ./dax-generator-namdEq-MtimesNSeq.py ### The dax-generator script is executed and it creates dax.xml file. ...
We submit the jobs and check the status of the job as follows:
### To submit the job $ ./submit.bash ### To check the status $ pegasus-status ### or you can also check with the condor_q command $ condor_q username ### username is your login ID
Go to the directory
tutorial-pegasus-namd/Exercises/MtimesNseqEx4. All the input files related to run 1000 sequential NAMD jobs are in the file
inputsM1000N50.tar.gz. The input files were already generated to save time. Uncompress the files by running the command
tar -xvzf inputsM1000N50.tar.gz that will create a directory
inputs/ containing all the NAMD input files. The input files represent 1000 parallel, each of 50-sequential NAMD jobs. Include this information in the dax-generator.
- [x] Pegasus requires dax.xml, sites.xml and pegasusrc files. These files contain the information about executable, input and output files and the relation between them while executing the jobs.
- [x] It is convenient to generate the xml files via scripts. In our example, dax.xml is generated via python script and sites.xml is generated via bash script.
- [x] To implement a new workflow, edit the existing dax-generator, sites-generator and submit scripts. In the above examples, we modified the workflow for the single NAMD job to implement the workflows of N-sequential and M-parallel, N-sequential jobs.
- Pegasus Documentation Pegasus documentation page
- OSG QuickStart. Getting started with the Open Science Grid (OSG)
- HTCondor Manual. Manual for the High Throughput Condor software to schedules the jobs on OSG
This page was updated on Nov 12, 2019 at 00:45 from tutorials/tutorial-pegasus-namd/README.md.