Parallel Processing


One of the primary goals of MsPASS was a framework to make parallel processing of seismic data no more difficult than running a typical python script on a desktop machine. In modern IT lingo our goals was a “scalable” framework. The form of parallelism we exploit is a one of a large class of problems that can reduced to what is called a directed cyclic graph (DAG) in computer science. Any book on “big data” will discuss this concept. Chapter 1 of Daniel (2019) has a particularly useful description using a cooking analogy. Our framework supports two well developed systems for handling such problems called Spark and Dask. Dask is the default.

An important perspective on this issue is that classical seismic processing systems concepts are well matched to the DAG Dask and Spark use for scheduling. Classic seismic reflection processing systems runs a data set through a series of processing algorithms. Each processor acts on either a single signal at at time (e.g. time-domain filtering) or a group of signals (what we call a TimeSeriesEnsmble in MsPASS) to produce an output of one or more modified signals. e.g. stacking takes an ensemble and produces a single output while migration takes an input ensemble and produces a (modified) ensemble. All these examples can be implemented in MsPASS framework to take advantage of the repetitious nature of the processors. That is, all can be the thought of as black boxes that take one or more seismic data objects as input and emit more or modified data objects (not necessarily of the same type or number as the inputs). A full processing workflow is assembled by chaining a series of processing steps with data moving between the processing steps by a system dependent process. Dask and Spark generalize this by fragmenting the processing into multiple processors and/or nodes moving the data between steps by a process largely opaque to you as the user. An added benefit that is less obvious is that lower-level parallel granularity is possible for a python function designed to exploit the capability. This user manual, however, focuses exclusively on algorithm level granularity. We refer the reader to extensive printed and online documentation for DASK and Spark for functionality needed to add parallelism inside an individual python function.

Data Model

A key concept to understand about both Spark and Dask is the data model. A simple way to view the concept is that we aim to break “our data” into a finite set of N things (objects) that are each to be handled identically. The raw input to any complete seismic data set fits this idea; the “data set” is a set of N raw signals. A serial version of processing a typical data set could be reduced to a loop over each of the N objects applying one or more algorithms to each datum one at a time. In Spark and Dask the data are treated collectively as a single data set. The “data set” is abstracted by a single container that is used to symbolically represents the entire data set even though it (normally) is not expected to fit in the computer’s memory. Spark refers to this abstraction as a Resiliant Distributed Dataset (RDD) while Dask calls the same thing a “bag”.

In MsPASS the normal content of a bag/RDD is a dataset made up of N MsPASS data objects: TimeSeries, Seismogram, or one of the ensemble of either of the atomic types. An implicit assumption in the current implementation is that any processing was proceeded by a data assembly and validation phase. Procedures for data import and data validation are described in this section Importing Data. A key concept here is that “importing” a data set to MsPASS means the entire data set has been assembled and loaded either into the gridfs file system internal to MongoDB or an index has been built for all data stored in local or cloud-based files. The entire data set can, if necessary, be a mix of the two because the MsPASS readers abstract the entire process of loading data for processing. In addition, any required normalization data (i.e. data in the site, channel, and/or source collections) must be loaded and cross-referencing validated as described in Normalization. Once the data set is fully assembled the bag/RDD defining the abstraction of the data is easily defined by a variant of the following code section:

from mspasspy.db.client import DBClient
from mspasspy.db.database import (Database,
dbname='exampledb'   # This sets the database name - this is an example
# code here can optionally produce a valid string defining query
# as a valid MongoDB argument of for find method.  Default is no query
# Example uses no normalization - most read use normalize (list) argument
# For spark this modification is needed - context needs to be defined earlier

The final result of this example script is a dask bag (RDD for the commented out variant), which here is assigned to the python symbol mybag. The bag is created by the MsPASS read_distributed_data function from the “cursor” object returned by the MongoDB find method. A “cursor” is an iterable form of a list that utilizes the same concept as Spark and Dask - the full thing does not always exist in memory but is a moving window into the data set. The read_distributed_data function is more or less a conversion between external storage and the processing framework (Dask or Spark) that use a common concept of a buffered list.

Functional Programming Concepts


A second key concept we utilize for processing algorithms in MsPASS is the abstraction of functional programming, which is a branch of programming founded on lambda calculus. For most seismologists that abstraction is likely best treated only as a foundational concept that may or may not be helpful depending on your background. It is important, however, because all the parallel processing algorithms utilize a functional programming construct to run an algorithm on a dataset abstracted with a bag or RDD. There are two forms we use in MsPASS with these general forms:



Noting that Spark calls the later operation the (more common) name reduce.

These two constructs can be thought of as black boxes that handle inputs as illustrated below:

  • simple figure here showing map and reduce in a graphical form -

We expand on each of these constructs below.

The map operator

A map operator takes one input and emits a modified version of the input as output. The inputs and outputs of a map are often the same type (e.g. a time-invariant filter), but not always (e.g the bundle algorithm takes a TimeSeriesEnsemble as and input and emits a SeismogramEnsemble). A concrete example for the application of a simple filter in dask is:

# Assume dbhandle is set as a Database class as above
d_in=read_distributed_data(dbhandle,cursor), "bandpass", freqmin=1, freqmax=5, object_history=True, alg_id='0')

This example applies the obpsy default bandpass filter to all data stored in the wf_TimeSeries collection for the database to which dbhandle points. The read_distributed_data line loads that data as a Dask bag we here call d_in. The map operator applies the algorithm defined by the symbol signals_filter to each object in d_in and stores the output in the created (new) bag d_out. The last line is way you tell dask to “go” (i.e. proceed with the calculations) and store the computed result in the d_compute. The idea and reasons for the concept of of “lazy” or “delayed” operation is discussed at length in various sources on dask (and Spark). We refer the reader to (LIST OF A FEW KEY URLS) for more on this general topic. The final output, which we chose above to give a new symbol name of d_compute, is bag containing the processed data.

The same construct in Spark, unfortunately, requires a different set of constructs for two reasons: (1) pyspark demands a functional programming construct called a lambda function, and (2) spark uses a different construct for handling delayed computations. The following example is the translation of the above to Spark:

# Assume dbhandle is set as a Database class as above and context is
# Spark context object also created earlier
d_in=read_distributed_data(dbhandle,cursor,format='spark',spark_context=context) d : signals.filter(d,"bandpass", freqmin=1, freqmax=5, object_history=True, alg_id='0'))

Notice the call to map in spark needs to be preceded by a call to the parallelize method of the SparkContext object, which is called inside read_distributed_data. That operator is more or less a constructor for the container that Spark calls an RDD that is assigned the symbol d_out in the example above. The following line, which from a programming perspective is a call to the map method of the RDD we call d_out, uses the functional programming construct of a lambda function. This tutorial in and this one by are good starting points.

Both scripts create a final processed data set python associates with the symbol d_compute. A potentially confusing issue for beginners is that the content of d_compute are largely opaque. The reason is that both a bag and RDD are designed to handle a data set that will not fit in memory. Dask and Spark have different methods for disaggregating the container, but most MsPASS workflows would normally terminate with a database save operation.

Reduce/fold operators

A second parallel construct we use is the the Reduce clause of the MapReduce paradigm that was a core idea in Hadoop (see for example the document in this link ) that was the ancestor of both Spark and Dask.

The generic problem of stacking (averaging) a set of signals is an example familiar to all seismologists that can be used to illustrate what a Reduce operator is. The following is a crude MsPASS serial implementation of stacking all the members of an ensemble:

for i in range(len(d.member)-1):
  stack += ensemble.member[i+1]

That code is pretty simple because the += operator is defined for the TimeSeries class and handles time mismatches. It is not robust for several reasons and could be done other ways, but that is not the key point. The point is that the operation is summing a set of TimeSeries objects to produce the single result stored with the symbol stack.

We will get to the rules that constrain Reduce operators in a moment, but it might be more helpful to you as a user to see how that algorithm translates into dask/spark. MsPASS has a parallel stack algorithm found here It is used in a parallel context as follows for dask:

res = ddb.fold(lambda a, b: stack(a, b))

For spark the syntax is identical but the name of the method changes to reduce:

res = rdd.reduce(lambda a, b: stack(a, b))

The stack symbol refers to a python function that is actually quite simple. You can view the source code here. It is simple because most of the complexity is hidden behind the += symbol that invokes that operation in C++ (TimeSeries::operator+= for anyone familiar with C++) to add the right hand side to the left hand side of the operator. The python function is also simplified significantly by the use of python decorator defined by this line in the stack source code:


which is a generic wrapper to adapt any suitable reduce function to MsPASS.

The final issue we need to cover in this section is what exactly is meant by the phrase “any suitable reduce function” at the end of the previous paragraph? To mesh with the reduce framework used by spark and dask a function has to satisfy the following rules :

  1. The first two arguments (a and b symbols in the example above) must define two instances of the same type that are to be combined in some way.

  2. The function must return an object of the same type as the inputs.

  3. The combination algorithm must be commutative and associative.

The commutative and associative restriction arises because in a parallel setting a type reduce operation like a summation is done on multiple processors and eventually summed to a single output. Which processor does what part of the sum is completely determined by the scheduler so an order cannot be assumed.

A simple summary of the role of reduce operators in algorithms is this: any operator that can be expressed mathematically as a summation operator is a candidate for a reduce. The stack example above involves summing a set of TimeSeries objects, but the approach can be used at lower levels. In particular, reduce is a commonly used tool to implement threading in pure python code that implements some summation operation. Turning the summation loop into a reduce operator can parallelize the loop. Users should consider that approach in writing pure python algorithms.


As noted previously MsPASS currently supports two different schedulers: Dask (the default) and Spark. Both do very similar things but are known to perform differently in different cluster environments. Users needing to push the system to the limits may need to evaluate which perform better in their environment.

In MsPASS we use Spark and Dask to implement the “master-worker” model of parallel computing. The “master” is the scheduler that hands off task to be completed by the workers. A critical issue this raises is how the data is handled that the workers are told to process? Both Spark and Dask do that through “serialization”. The schedulers move atomic data between processes by serializing the data and then having the other end deserialize it. How and when that happens is a decision made by the scheduler. That process is one of the primary limits on scalability of this framework. e.g. it is normal for a single worker calculation to be much slower than a simple loop implementation because of the serialization overhead. The default serialization for both PySpark (The native tongue of Spark is Scalar. PySpark is the python api.) and Dask (Python is the native tongue of Dask.) is pickle. It is important to recognize that if you write your own application in this framework the data object you pass to map and reduce operators must have a pickle operator defined. That function needs to be as fast as possible as it will be called a lot in a parallel environment.

Another limit on scalability of this framework is that before the computations, Dask and Spark need to create a task graph for task scheduling. Task scheduling breaks your program into many medium-sized tasks or units of computation. These tasks are typically a function call which in MsPASS usually involves passing a non-trivial amount of data to the task (one or more seismic data objects). The schedulers represent these tasks as nodes in a graph with links between nodes defining how data moves between tasks. The task scheduler uses this graph in a way that respects these data dependencies and leverages parallelism where possible. Multiple independent tasks can be run simultaneously that are are data driven. Usually this scheduling overhead is relatively small unless the execution time for processing is trivial.

For more information, the dask documentation found here is a good starting point.


Atomic Data Example

The simplest workflow is one that works only with atomic data (i.e. TimeSeries or Seismogram objects). The example example in the Data Model section above is of this type. The following fragment is similar with a few additional processing steps. It reads all data indexed in the data base as Seismogram objects, runs a demean operator, runs a simple bandpass filter, windows the data to a smaller range defined by the window_seis function defined at he top, it using the data start time, and then saves the results.

# read -> detrend -> filter -> window
# example uses dask scheduler
data = read_distributed_data(db, cursor)
data =,'demean')
data =,"bandpass",freqmin=0.01,freqmax=2.0)
# windowing is relative to start time.  300 s window starting at d.t0+200
data = d : WindowData(d,200.0,500.0,t0shift=d.t0))
data_out = data.compute()

Ensemble Example

This example needs to use function to build a query, put the query in a map call, and then run an ensemble process. Here is an untested prototype for this manual

def read_common_source_gather(db,collection,srcid):
  dbcol = db[collection]
  query = {"source_id" : srcid }
  # note with logic of this use we don't need to test for
  # no matches because distinct returns only not null source_id values dbcol
  cursor = dbcol.find(query)
  ensemble = db.read_ensemble(db,collection=collection)
  return ensemble

dbcol = db.wf_Seismogram
srcidlist = db.wf_Seismogram.distinct("source_id")
data = dask.bag.from_sequence(srcidlist)
data = srcid : read_common_source_gather(db,"wf_Seismogram",srcid))
data =,'demean')
data =,"bandpass",freqmin=0.01,freqmax=2.0)
# windowing is relative to start time.  300 s window starting at d.t0+200
data = d : WindowData(d,200.0,500.0,t0shift=d.t0))
data_out = data.compute()

New Organization for discussion

Cluster fundamentals

Overview of what one has to deal with to configure a parallel system in a distributed cluster versus a multicore workstation. Here are things I can think of we need to discuss:

  • batch Schedulers

  • node-to-node communications

  • containers in a distributed environment

  • to shard or not to shard, that is the question

  • io performance issues and choices (relates to file system related configuration)


subsections for each of the above topics centered on example.

I think we should reorganize the script to have related setups grouped by the categories we choose for this user manual section (as much as possible - there may be some order dependence)

Start of old section



Some configuration will be needed to run MsPASS in a HPC system or a departmental cluster. The reason is that the environment of an HPC cluster has numerous complications not found on a desktop system. The example we give here is what we use for testing the system on Stampede2 at TACC. This section can be thought of as a lengthy explanation centered on the example in our github page for configuring MsPASS in a large, distributed memory system like TACC’s Stampede2. To read this page we recommend you open a second winodw or tab on your web browser to the current file in the mspass source code directory called scripts/tacc_examples/ The link to the that file you can view on your web browser is here. We note there is an additional example there for running MsPASS on a single node at TACC called scripts/tacc_examples/ you can access directly here, The single node setup is useful for testing and may help your understanding of what is needed by being much simpler. We do not discuss that example further here, however, because a primary purpose for using MsPASS is processing data in a large HPC cluster like TACC.

nxt para needs to say tis is a shelll script and the section below are grouped by functional issues then list them (singularity, mongodb, and ?)

Workload Manager Setup

It uses a workload manager software installed there called Slurm and the associated command keyword SBATCH. If your system does not have Slurm there will be something similar (notably Moab or Torque) that you will need to substitute. Perhaps obvious but things like file system configuration will need changes to match your local environment.

Slurm is used as a batch control system to schedule a “batch” job on a large cluster like Stampede2. Batch jobs are submitted to be run on compute notes by submitting a file the command line tool called sbatch. The submitted file is a expected to be a unix shell script that runs your “batch job”. To be run under Slurm the shell script normally defines a set of run configuration parameters defined in the first few lines of the script. Here is a typical examples:

#SBATCH -J mspass          # Job name - change as approrpiate
#SBATCH -o mspass.o%j      # Name of stdout output file redirection
#SBATCH -p normal          # Queue (partition) name
#SBATCH -N 2               # Total # of nodes requested (2 for this example)
#SBATCH -n 2               # Total # of mpi tasks
#SBATCH -t 02:00:00        # Run time (hh:mm:ss)

This example requests 2 nodes (-N 2) for a run time of 2 hours (-t line) submitted to TACC’s “normal” queue (-p normal). Note the Slurm configuration parameters are preceded by the keyword #SBATCH. The lines begin with the “#” symbol which the unix shell will treat as a comment. That is done for a variety of reasons but one important practical one is to test the syntax of a script on a head node without having to submit the full job.

MsPASS was designed to be run in a container. For a workstation environment we assume the container system being used is docker. Running MsPASS with docker is described on this wiki page. All HPC systems we know have a docker compatible system called singularity. Singularity can be thought of as docker for a large HPC cluster. The most important feature of singularity for you as a user is that it uses exactly the same container file as docker. i.e. you “pull” the docker container and that is used by singularity in a very similar fashion to the way it used by docker as follows:

singularity build mspass.simg docker://wangyinz/mspass

For more about running MsPASS with singularity consult our wiki page found here. Since our examples here were constructed on TACC’ Stampede2 you may also find it useful to read their page on using singularity found here

There is a single node mode you may want to run for testing. You can find an example of how to configure Stampede2 to run on a single node in the MsPASS scripts/tacc_examples found on github here. We focus is manual on configuration for a production run using multiple nodes, that is a primary purpose of using MsPASS for data processing. The example we give here is the

There are two ways we could deploy our system on stampede2, which are single node mode and distributed mode. You could refer those two job script in our /scripts/tacc_examples folder in our source code. Here we would introduce the common parts and elements in both scripts.

In both modes, we would specify the working directory and the place we store our docker image. That’s why these two lines are in the job scripts:

# working directory
# directory where contains docker image

The paths for these two variables can be changed according to your case and where you want to store the image. And it doesn’t matter if the directory doesn’t exist, the job script would create one if needed.

Then we define the SING_COM variable to simplify the workflow in our job script. On Stampede2 and most of HPC systems, we use Singularity to manage and run the docker images. There are many options to start a container using singularity, which you could refer to their documentation. And for those who are not familiar with Singularity, here is a good source to get start with.

# command that start the container
module load tacc-singularity
SING_COM="singularity run $MSPASS_CONTAINER"

Then we create a login port based on the hostname of our primary compute node we have requested. The port number is created in a way that guaranteed to be unique and availale on your own machine. After the execution of your job script, you would get the ouput file, and you could get the url for accessing the notebook running on your compute node. However, from you own computer, you should use this login port to access it instead of 8888 which typically is the port we will be using in jupyter notebook because we reserve the port for transmitting all the data and bits through the reverse tunnel.

NODE_HOSTNAME=`hostname -s`
LOGIN_PORT=`echo $NODE_HOSTNAME | perl -ne 'print (($2+1).$3.$1) if /c\d(\d\d)-(\d)(\d\d)/;'`

Next, we create reverse tunnel port to login nodes and make one tunnel for each login so the user can just connect to stampede.tacc. The reverse ssh tunnel is a tech trick that could make your own machine connect to the machines in the private TACC network.

for i in `seq 4`; do
  ssh -q -f -g -N -R $LOGIN_PORT:$NODE_HOSTNAME:8888 login$i

For single node mode, the last thing we need to do is to start a container using the command we defined before:


Here we set the environment variables inside the container using this syntactic sugar SINGULARITYENV_XXX. For more information, you could view the usage here. We define and set different variables in different containers we start because in our, we define different bahavior under different MSPASS_ROLE so that for each role, it will execute the bash script we define in the Though it looks complicated and hard to extend, this is prabably the best way we could do under stampede2 environment. In above code snippet, we basically start the container in all-in-one way.

There are more other ways we start a container and it depends on what we need for the deployment. You could find more in the job script. For example, we start a scheduler, a dbmanager and a front-end jupyter notebook in our primary compute node and start a spark/dask worker and a MongoDB shard replica on each of our worker nodes. Also you could find that the environment variables needed are different and you could find the corresponding usage in the script in our source code. We hide the implementation detail and encapsulate it inside the Dockerfile. One more thing here is we specify number of nodes in our sbatch options, for example 4, stampede2 would reserve 4 compute nodes for us, and we would use 1 node as our primary compute nodes and 3 nodes as our worker nodes. Therefore, if you need 4 worker nodes, you should sepcify 5 as your sbatch option for nodes.