20 petaflops simulation of proteins suspensions in crowding conditions (Contributo in atti di convegno)

Type
Label
  • 20 petaflops simulation of proteins suspensions in crowding conditions (Contributo in atti di convegno) (literal)
Anno
  • 2013-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1145/2503210.2504563 (literal)
Alternative label
  • Bernaschi M.; Bisson M.; Fatica M.; Melchionna S. (2013)
    20 petaflops simulation of proteins suspensions in crowding conditions
    in Conference for High Performance Computing
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Bernaschi M.; Bisson M.; Fatica M.; Melchionna S. (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://www.scopus.com/inward/record.url?eid=2-s2.0-84899682667&partnerID=q2rCbXpz (literal)
Note
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Istituto Applicazioni Calcolo (CNR-IAC), Consiglio Nazionale Delle Ricerche, Rome, Italy; Istituto Processi Chimico-Fisici (CNR-IPCF), Consiglio Nazionale Delle Ricerche, Rome, Italy; Nvidia Corporation, Santa Clara, CA, United States (literal)
Titolo
  • 20 petaflops simulation of proteins suspensions in crowding conditions (literal)
Abstract
  • We present performance results for the simulation of proteins suspensions in crowding conditions obtained with MU-PHY, a computational platform for multi-scale simulations of real-life biouidic problems. Previous versions of MU-PHY have been used in the past for the simulation of blood ow through the human coronary arteries and DNA translocation across nanopores. The simulation exhibits excellent scalability up to 18; 000 K20X Nvidia GPUs and achieves almost 20 Petaops of aggregate sustained performance with a peak performance of 27.5 Petaops for the most intensive computing component. Those figures demonstrate once again the exibility of MUPHY in simulating biouidic phenomena, exploiting at their best the features of the architecture in use. Preliminary results were obtained in the present case on a completely different platform, the IBM Blue Gene/Q. The combination of novel mathematical models, computational algorithms, hardware technology, code tuning and parallelization techniques required to achieve these results are presented. Copyright 2013 ACM. (literal)
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