Prototype implementations of Workflow Management System (WMS) components and underlying algorithms
Captures the behavior of a real-world system with as little bias as possible via validated simulation models
Low ratio of simulation time to simulated time, ability to run large simulations on a single computer with low compute, memory, and energy footprints
Reproduction or repetition of published results by a party working independently using the same/different simulation models
Simulation-driven pedagogic modules for teaching parallel and distributed computing
The eduWRENCH Project provides a set of simulation-driven, self-contained, modules that span a range of proficiency levels, from college freshmen to graduate students, and for assessing the pedagogic effectiveness of simulation-drive pedagogy
WRENCH enables novel avenues for scientific workflow use, research, development, and education in the context of large-scale scientific computations and data analyses. WRENCH is an open-source library for developing simulators. WRENCH exposes several high-level simulation abstractions to provide high-level building blocks for developing custom simulators.
WRENCH provides a software framework that makes it possible to simulate large-scale hypothetical scenarios quickly and accurately on a single computer, obviating the need for expensive and time-consuming trial and error experiments.
WRENCH enables scientists to make quick and informed choices when executing their workflows, software developers to implement more efficient software infrastructures to support workflows, and researchers to develop novel efficient algorithms to be embedded within these software infrastructures.
H. Casanova, R. Ferreira da Silva, R. Tanaka, S. Pandey, G. Jethwani, W. Koch, S.
Albrecht, J. Oeth, and F. Suter
Future Generation Computer Systems, 2020. DOI: 10.1016/j.future.2020.05.030
R. Tanaka, R. Ferreira da Silva, and H. Casanova
Workshop on Education for High-Performance Computing (EduHPC), 2019. DOI: 10.1109/EduHPC49559.2019.00006
R. Ferreira da Silva, H. Casanova, A. Orgerie, R. Tanaka, E. Deelman, and F.
Journal of Computational Science, 2020. DOI: 10.1016/j.jocs.2020.101157
H. Casanova, S. Pandey, J. Oeth, R. Tanaka, F. Suter, and R. Ferreira da Silva
13th Workshop on Workflows in Support of Large-Scale Science (WORKS’18), 2018. DOI: 10.1109/WORKS.2018.00013.