Improving the scalability of a charge detection mass spectrometry workflow. McClary, S., Henschel, R., Thota, A., Brunst, H., & Draper, B. In Proceedings of the XSEDE16 Conference on Diversity, Big Data, and Science at Scale (XSEDE16), volume 17-21-July, pages 8, 2016. Association for Computing Machinery. ![Improving the scalability of a charge detection mass spectrometry workflow [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website doi abstract bibtex The Indiana University (IU) Department of Chemistry's Martin F. Jarrold (MFJ) Research Group studies a specialized technique of mass spectrometry called Charge Detection Mass Spectrometry (CDMS). The goal of mass spectrometry is to determine the mass of chemical and biological compounds, and with CDMS, the MFJ Research Group is extending the upper limit of mass detection. These researchers have developed a scientific application, which accurately analyzes raw CDMS data generated from their mass spectrometer. This paper explains the comprehensive process of optimizing the group's workflow by improving both the latency and throughput of their CDMS application. These significant performance improvements enabled high efficiency and scalability across IU's Advanced Cyberinfrastructure; overall, this analysis and development resulted in a 25x speedup of the application. © 2016 ACM.
@inproceedings{
title = {Improving the scalability of a charge detection mass spectrometry workflow},
type = {inproceedings},
year = {2016},
keywords = {Application performance,Big data,Charge detection,Chemic,Chemical compounds,Chemical detection,Mass spect},
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abstract = {The Indiana University (IU) Department of Chemistry's Martin F. Jarrold (MFJ) Research Group studies a specialized technique of mass spectrometry called Charge Detection Mass Spectrometry (CDMS). The goal of mass spectrometry is to determine the mass of chemical and biological compounds, and with CDMS, the MFJ Research Group is extending the upper limit of mass detection. These researchers have developed a scientific application, which accurately analyzes raw CDMS data generated from their mass spectrometer. This paper explains the comprehensive process of optimizing the group's workflow by improving both the latency and throughput of their CDMS application. These significant performance improvements enabled high efficiency and scalability across IU's Advanced Cyberinfrastructure; overall, this analysis and development resulted in a 25x speedup of the application. © 2016 ACM.},
bibtype = {inproceedings},
author = {McClary, S and Henschel, R and Thota, A and Brunst, H and Draper, B},
doi = {10.1145/2949550.2949563},
booktitle = {Proceedings of the XSEDE16 Conference on Diversity, Big Data, and Science at Scale (XSEDE16)}
}
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