Achievements

Sano will use the fastest Polish supercomputers located at the Cyfronet AGH Academic Computer Centre in Kraków. The Prometheus currently in place has a computing power of over 50,000 high-end PCs, consists of 15 racks, each of which contains 144 servers and weighs over 30 tonnes. Between 2021 and 2022 a major extension of Cyfronet’s computing power is planned, also to benefit Sano.

Examples of publications

The EurValve model execution environment

Bubak, K. Czechowicz, T. Gubała, D. R. Hose, M. Kasztelnik, M. Malawski, J. Meizner, P. Nowakowski, and S. Wood

Royal Society, 11 December 2020,

DOI: https://doi.org/10.1098/rsfs.2020.0006

PRIMAGE project: predictive in silico multiscale analytics to support childhood cancer personalised evaluation empowered by imaging biomarkers

Martí-Bonmatí, Á. Alberich-Bayarri, R. Ladenstein, I. Blanquer, J. Damian Segrelles, L. Cerdá-Alberich, P. Gkontra, B. Hero, J.M. García-Aznar, D. Keim, W. Jentner, K. Seymour, A. Jiménez-Pastor, I. González-Valverde, B. Martínez de las Heras, S. Essiaf, D. Walker, M. Rochette, M. Bubak, J. Mestres, M. Viceconti, G. Martí-Besa, A. Cañete, P. Richmond, K.Y. Wertheim, T. Gubala, M. Kasztelnik, J. Meizner, P. Nowakowski, S. Gilpérez, A. Suárez, M. Aznar, G. Restante and E. Neri

European Radiology Experimental, 2020, 4: 22

DOI: https://doi.org/10.1186/s41747-020-00150-9

Sano will use the fastest Polish supercomputers located at the Cyfronet AGH Academic Computer Centre in Kraków. The Prometheus currently in place has a computing power of over 50,000 high-end PCs, consists of 15 racks, each of which contains 144 servers and weighs over 30 tonnes. Between 2021 and 2022 a major extension of Cyfronet’s computing power is planned, also to benefit Sano.

Examples of publications

The EurValve model execution environment

Bubak, K. Czechowicz, T. Gubała, D. R. Hose, M. Kasztelnik, M. Malawski, J. Meizner, P. Nowakowski, and S. Wood

Royal Society, 11 December 2020,

DOI: https://doi.org/10.1098/rsfs.2020.0006

PRIMAGE project: predictive in silico multiscale analytics to support childhood cancer personalised evaluation empowered by imaging biomarkers

Martí-Bonmatí, Á. Alberich-Bayarri, R. Ladenstein, I. Blanquer, J. Damian Segrelles, L. Cerdá-Alberich, P. Gkontra, B. Hero, J.M. García-Aznar, D. Keim, W. Jentner, K. Seymour, A. Jiménez-Pastor, I. González-Valverde, B. Martínez de las Heras, S. Essiaf, D. Walker, M. Rochette, M. Bubak, J. Mestres, M. Viceconti, G. Martí-Besa, A. Cañete, P. Richmond, K.Y. Wertheim, T. Gubala, M. Kasztelnik, J. Meizner, P. Nowakowski, S. Gilpérez, A. Suárez, M. Aznar, G. Restante and E. Neri

European Radiology Experimental, 2020, 4: 22

DOI: https://doi.org/10.1186/s41747-020-00150-9