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To facilitate evaluation of workflow algorithms and systems on a range of workflow sizes, we have developed a set of synthetic workflow generators. These generators use the information gathered from actual executions of scientific workflows on the Grid as well as our understanding of the processes behind these workflows to generate realistic, synthetic workflows resembling those used by real world scientific applications.


The code used to generate all of the workflows below, and many others, is available from the GitHub repository. The java workflow generator sometimes generates negative task runtimes, so watch out for that.

Pegasus Workflows

These workflows come from a paper by Bharathi, et al. [1]. There is another paper with more information about the workflows by Juve, et al. [2].

A large collection of DAXes similar to the ones listed below is available here. Note that it is about 375 MB.

Workflow Type



The Montage application created
by NASA/IPAC stitches together multiple
input images to create
custom mosaics of the sky.

25 Node DAX
50 Node DAX
100 Node DAX
1000 Node DAX

The CyberShake workflow is used
by the Southern Calfornia Earthquake
Center to characterize
earthquake hazards in a region.

30 Node DAX
50 Node DAX
100 Node DAX
1000 Node DAX

The epigenomics workflow created
by the USC Epigenome Center
and the Pegasus Team is used to
automate various operations
in genome sequence processing.

24 Node DAX
46 Node DAX
100 Node DAX
997 Node DAX

LIGO Inspiral Analysis
LIGO's Inspiral Analysis workflow
is used to generate and
analyze gravitational waveforms
from data collected during the
coalescing of compact binary systems.

30 Node DAX
50 Node DAX
100 Node DAX
1000 Node DAX

The SIPHT workflow, from the
bioinformatics project at Harvard,
is used to automate the search for
untranslated RNAs (sRNAs) for bacterial
replicons in the NCBI database.

30 Node DAX
60 Node DAX
100 Node DAX
1000 Node DAX

Ramakrishnan and Gannon Workflows

These workflows come from a report by Ramakrishnan and Gannon [3].

Workflow TypeFigure in ReportExampleDAX
LEAD Mesoscale MeteorologyFigure 1leadmm.xml
LEAD ARPS Data Analysis SystemFigure 2


LEAD Data Mining WorkflowFigure 3leaddm.xml
Storm Surge SCOOP WorkflowFigure 4




Floodplain MappingFigure 5floodplain.xml
GlimmerFigure 6glimmer.xml
Gene2LifeFigure 7gene2life.xml
Motif NetworkFigure 8




MEME-MASTFigure 9mememast.xml
Molecular SciencesFigure 10molsci.xml
Avian FluFigure 11




caDSRFigure 12cadsr.xml
Pan-STARRS LoadFigure 13




Pan-STARRS MergeFigure 14




McStasFigure 15mcstas.xml


[1] R. F. da Silva, W. Chen, G. Juve, K. Vahi, E. Deelman. Community Resources for Enabling Research in Distributed Scientific Workflows. 10th IEEE International Conference on e-Science (eScience 2014)

[2] S. Bharathi, A. Chervenak, E. Deelman, G. Mehta, M.-H. Su, and K. Vahi, “Characterization of Scientific Workflows”, 3rd Workshop on Workflows in Support of Large Scale Science (WORKS 08), 2008.

[3] Gideon Juve, Ann Chervenak, Ewa Deelman, Shishir Bharathi, Gaurang Mehta, and Karan Vahi , "Characterizing and Profiling Scientific Workflows", Future Generation Computer Systems , 29:3, pp. 682–692, March 2013 .

[4] L. Ramakrishnan and D. Gannon, "A Survey of Distributed Workflow Characteristics and Resource Requirements", Indiana University Technical Report TR671, 2008.


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