%0 Generic %1 qian2017simon %A Qian, Penny Xuran %A Cai, Maxwell Xu %A Zwart, Simon Portegies %A Zhu, Ming %D 2017 %K monitor simulation %T SiMon: Simulation Monitor for Computational Astrophysics %U http://arxiv.org/abs/1706.09831 %X Scientific discovery via numerical simulations is important in modern astrophysics. This relatively new branch of astrophysics has become possible due to the development of reliable numerical algorithms and the high performance of modern computing technologies. These enable the analysis of large collections of observational data and the acquisition of new data via simulations at unprecedented accuracy and resolution. Ideally, simulations run until they reach some pre-determined termination condition, but often other factors cause extensive numerical approaches to break down at an earlier stage. In those cases, processes tend to be interrupted due to unexpected events in the software or the hardware. In those cases, the scientist handles the interrupt manually, which is time-consuming and prone to errors. We present the Simulation Monitor (SiMon) to automatize the farming of large and extensive simulation processes. Our method is light-weight, it fully automates the entire workflow management, operates concurrently across multiple platforms and can be installed in user space. Inspired by the process of crop farming, we perceive each simulation as a crop in the field and running simulation becomes analogous to growing crops. With the development of SiMon we relax the technical aspects of simulation management. The initial package was developed for extensive parameter searchers in numerical simulations, but it turns out to work equally well for automating the computational processing and reduction of observational data reduction.

@misc{qian2017simon, abstract = {Scientific discovery via numerical simulations is important in modern astrophysics. This relatively new branch of astrophysics has become possible due to the development of reliable numerical algorithms and the high performance of modern computing technologies. These enable the analysis of large collections of observational data and the acquisition of new data via simulations at unprecedented accuracy and resolution. Ideally, simulations run until they reach some pre-determined termination condition, but often other factors cause extensive numerical approaches to break down at an earlier stage. In those cases, processes tend to be interrupted due to unexpected events in the software or the hardware. In those cases, the scientist handles the interrupt manually, which is time-consuming and prone to errors. We present the Simulation Monitor (SiMon) to automatize the farming of large and extensive simulation processes. Our method is light-weight, it fully automates the entire workflow management, operates concurrently across multiple platforms and can be installed in user space. Inspired by the process of crop farming, we perceive each simulation as a crop in the field and running simulation becomes analogous to growing crops. With the development of SiMon we relax the technical aspects of simulation management. The initial package was developed for extensive parameter searchers in numerical simulations, but it turns out to work equally well for automating the computational processing and reduction of observational data reduction.}, added-at = {2017-06-30T10:10:48.000+0200}, author = {Qian, Penny Xuran and Cai, Maxwell Xu and Zwart, Simon Portegies and Zhu, Ming}, biburl = {https://www.bibsonomy.org/bibtex/2ffeead870e74cff075b6d91a3f8ede40/miki}, description = {[1706.09831] SiMon: Simulation Monitor for Computational Astrophysics}, interhash = {916cd21d958e77812b62c11016ce00f0}, intrahash = {ffeead870e74cff075b6d91a3f8ede40}, keywords = {monitor simulation}, note = {cite arxiv:1706.09831Comment: 13 pages, 6 figures, Accepted for publication in Publications of the Astronomical Society of the Pacific (PASP)}, timestamp = {2017-06-30T10:10:48.000+0200}, title = {SiMon: Simulation Monitor for Computational Astrophysics}, url = {http://arxiv.org/abs/1706.09831}, year = 2017 }