Plasma fusion code coupling using scalable I/O services and scientific workflows

Abstract

In order to understand the complex physics of mother nature, physicist often use many approximations to understand one area of physics and then write a simulation to reduce these equations to ones that can be solved on a computer. Different approximations lead to different equations that model different physics, which can often lead to a completely different simulation code. As computers become more powerful, scientists can either write one simulation that models all of the physics or they produce several codes each for different portions of the physics and then 'couple' these codes together. In this paper, we concentrate on the latter, where we look at our code coupling approach for modeling a full device fusion reactor. There are many approaches to code coupling. Our first approach was using Kepler workflows to loosely couple three codes via files (memory-to-disk-to-memory coupling). This paper describes our new approach moving towards using memory-to-memory data exchange to allow for a tighter coupling. Our approach focuses on a method which brings together scientific workflows along with staging I/O methods for code coupling. Staging methods use additional compute nodes to perform additional tasks such as data analysis, visualization, and NxM transfers for code coupling. In order to transparently allow application scientist to switch from memory to memory coupling to memory to disk to memory coupling, we have been developing a framework that can switch between these two I/O methods and then automate other workflow tasks. Our hybrid approach allows application scientist to easily switch between in-memory coupling and file-based coupling on-the-fly, which aids debugging these complex configurations.

Experiences Applying Data Staging Technology in Unconventional Ways

“…In this case, a global security envelope that extends beyond a single resource to protect data must be developed. ORNL has developed a primitive version of this for use with the Kepler workflow engine by using an SSH proxy to tunnel into the HPC resource from outside [21]. This tunnel is then used to communicate between the compute processes and the external workflow control system.…”

In-situ feature-based objects tracking for data-intensive scientific and enterprise analytics workflows

“…The couplings,interactions and coordination associated with data-intensive scientific workflows can be expressed using three canonical workflow patterns [9,10] as below:…”

Hobbes

“…Instead, we are interested in workflows that execute simulation, analysis, and visualization concurrently. Examples of existing exploratory analytics applications include coupling shock physics with ParaView for fragment detection [21], a Quantum Monte Carlo (QMC) materials code coupled with a service to generate observables in a different coordinate system [29], and the Pixie3D magnetohydrodynamics (MHD) code coupled to PixiePlot and ParaView [24].…”