Scientific workflows are a cornerstone of modern scientific computing, and are used widely across scientific domains. Workflow systems provide abstraction and automation for describing complex computational applications that require efficient and robust management of large volumes of data on high-performance compute resources.

At OLCF, we offer users with a selection of cutting-edge workflow systems that are easy-to-use while providing a comprehensive set of features for supporting a large range of use-cases from science domains.

What is a Workflow?
Many computationally intensive scientific applications have been framed as workflows that execute on compute platforms at platform scales. Scientific workflows are typically described as Directed Acyclic Graphs (DAGs) in which vertices represent tasks and edges represent dependencies between tasks, as defined by application-specific semantics. Workflows can also be composed of dynamic behaviors (e.g., loops, conditionals, etc.).
What a workflow can do?
A workflow can automate analysis of terabyte-scale data sets, be composed of millions of individual tasks, require coordination between tasks, manage tasks that execute for miliseconds to hours, and can process data streams, files, and data placed in object stores. The computations can be single core workloads, loosely coupled computations (like MapReduce), or tightly coupled (like MPI-based parallel programs).
Which workflow system should I use?
Identifying the best workflow system to use is key for obtaining experiment results with good performance (i.e., turnaround time from experiment definition, to execution, to fetching results). The present documentation provides some information on how each supported system could benefit your use-case, and how to design your workflow.
How can we help?
In addition to providing documentation, one of the goals of the OLCF’s workflows team is to engage with users to guide them on describing their workflow applications which may include (i) understanding the use-case, (ii) identifying the need for workflows, (iii) determining the most suited workflow system as well as OLCF resource, and (iv) helping designing and executing the workflow on OLCF resources.


To learn the basics about workflows and distributed computing, see this set of pedagogic modules that will introduce you to the workflow model of computation that is used in many scientific applications.

Running Workflows on OLCF Resources

Due to the increasing need to support workflows, dedicated workflow systems were developed to provide abstractions for creating, executing, and adapting workflows conveniently and efficiently while ensuring portability. While these efforts are all worthwhile individually, there are now hundreds of independent workflow systems. At OLCF, we are constantly evaluating and refining the selection of workflow systems made available to users. Below, you will find a list of current frameworks natively supported in our Systems:

Workflow System OLCF System
Ensemble Toolkit (EnTK) Summit
Parsl Summit
Swift/T Summit