![](media/image1.jpeg){width="1.4791666666666667in"
height="0.4791666666666667in"} **Advanced Scientific Computing
Research**

**Computer Science**

**FY 2006 Accomplishment**

**Scientific Process Automation**

## Terence Critchlow, Lawrence Livermore National Laboratory

#### Ilkay Altintas, San Diego Supercomputer Center

#### Bertram Ludaescher, University of California, Davis

#### Steve Parker, University of Utah 

#### Mladen Vouk, North Carolina State University

*Scientific Data Management Center*

Summary

***Science in many disciplines increasingly requires data-intensive and
compute-intensive information technology (IT) solutions for scientific
discovery. Scientific applications with these requirements range from
the understanding of biological processes at the sub-cellular level of
"molecular machines" (as is common, e.g., in genomics and proteomics),
to the level of simulating nuclear fusion reactions and supernova
explosions in astrophysics. A practical bottleneck for more effective
use of available computational and data resources is often in the IT
knowledge of the end-user; in the design of resource access and use of
processes; and the corresponding execution environments, i.e., in the
scientific workflow environment of end user scientists. The goal of the
Kepler/SPA thrust of the SDM Center is to provide solutions and products
for effective and efficient modeling, design, configurability,
execution, and reuse of scientific workflows.***

![](media/image3.png){width="4.833333333333333in"
height="2.7493055555555554in"}*promoter identification workflow* or PIW.
To clearly identify co-regulated groups of genes, high throughput
computational molecular biology tools are first needed that are scalable
for carrying out a variety of tasks such as identifying DNA sequences of
interest, comparison of DNA sequences, and identification of
transcription factor binding sites, etc. Some of these steps can be
executed by querying web-accessible databases and computation resources.
However, using web sources \"as-is\" to enact scientific workflows
required many manual and thus time-consuming and error-prone steps. It
was thus desirable to automate scientific workflows such as the PIW as
much as possible. A conventional, script-based solution is sometimes
used to derive the specific requirements of a workflow. This was the
case also for PIW. However, script-based prototypes are only an initial
step since they lack ease of use, configurability, reusability,
provenance tracking etc. which are precisely the strengths of
workflow-based approaches. Among the various open source workflow
technologies, Ptolemy II (on which Kepler is built) offers a powerful
workflow design interface, flexible execution models including for
pipelined-execution, and nested workflows which help in taming
complexity.

Similar to the PIW case, we worked closely with additional science
driver groups, specifically TSI and FSP, and built first custom, then
more general workflow support solutions. Results have been excellent and
very well received. Figures 3 illustrates the translation of a TSI (Dr.
Swesty) workflow into Kepler. Of course, behind each of the steps shown
in the figures are a number of more sophisticated and details processes
that perform actual computations, move, aggregate, and transform the
data, and deliver the final end-user product. In the context of TSI,
basic scientific challenges center around the development of a standard
model of supernovae core collapse and related processes. Underlying
challenges related to simulations, data analysis and data manipulation
include scalable parallel numerical algorithms for solution of large,
often sparse linear systems, flow equations, and large eigen-value
problems, running of simulation on supercomputers, movement of large
amounts of data over large distances, collaborative visualization and
computational steering, and collection of appropriate process and
simulation related status and provenance information. This requires
interdisciplinary teams of astrophysicists, nuclear physicists, fusion
physicist, applied mathematicians, computer scientists, etc. The general
underlying "template" (and the potential role-model for future workflow
construction and management "wizards") is amazingly similar: large-scale
parallel computations and steering (hundreds of processors, gigabytes of
memory, hours to weeks of CPU time), data-movement and reduction
(terabytes of data), visualization and analytics (interactive,
retrospective, and auditable). ![](media/image4.png){width="3.125in"
height="2.4916666666666667in"}

Figure 3: TSI workflow

**For further information on this subject contact:**

Terence Critchlow

Lawrence Livermore National Laboratory

Critchlow@llnl.gov

\(925\) 423-5682