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BEGIN:VEVENT
DTSTAMP:20260712T084647Z
DTSTART;VALUE=DATE-TIME:20181105T130000
DTEND;VALUE=DATE-TIME:20181105T140000
SUMMARY:Martin Weigel (Coventry)
TZID:Europe/London
UID:20181105-8a1785d865e8039f01663e7fa5536b2d@warwick.ac.uk
CREATED:20181026T092719Z
DESCRIPTION:Monte Carlo methods for massively parallel architectures Whil
 e Moore's law of semiconductors has ensured for over forty years that th
 e next generation of processors works significantly faster than the curr
 ent one\, for the last ten years or so serial code has not seen any spee
 d-up from new hardware which\, instead\, achieves performance improvemen
 ts only from packing more and more cores onto a single die. As a consequ
 ence\, scientists working with computer simulations need to move away fr
 om intrinsically serial algorithms to find new approaches that can make 
 good use of potentially millions of computational cores. Monte Carlo met
 hods based on Markov chains are intrinsically serial and hence cannot be
  straightforwardly parallelized. For systems with short-range interactio
 ns\, it it is possible to use domain decompositions to update several de
 grees of freedom simultaneously. A complementary approach simulates seve
 ral chains in parallel\, be it at different temperatures such as in repl
 ica-exchange Monte Carlo or at the same temperature by simply pooling th
 e statistics from independent runs. I give an overview of parallel imple
 mentations of Monte Carlo methods in statistical physics and\, in partic
 ular\, focus on two especially promising approaches: the first is a para
 llel variant of the multicanonical simulation method that uses independe
 nt walkers to speed up the convergence and shows close to perfect scalin
 g up to 105 threads. The second approach is a sequential Monte Carlo met
 hod known as population annealing\, that simulates a large population of
  configurations at the same temperature and then uses resampling and suc
 cessive cooling to propagate the population. This approach is particular
 ly suitable for parallel computing\, and I present an efficient GPU impl
 ementation. A number of improvements turn this approach into a fully ada
 ptive algorithm for the simulation of systems with complex free-energy l
 andscapes. References M. Weigel\, Monte Carlo methods for massively para
 llel computers\, in: "Order\, Disorder and Criticality"\, Vol. 5\, ed. Y
 u. Holovatch (World Scientific\, Singapore\, 2018)\, pp. 271-340. J. Gro
 ss\, J. Zierenberg\, M. Weigel\, and W. Janke\, Massively parallel multi
 canonical simulations\, Comput. Phys. Commun. 224\, 387 (2018). L. Yu. B
 arash\, M. Weigel\, M. Borovský\, W. Janke\, and L. N. Shchur\, GPU acce
 lerated population annealing algorithm\, Comput. Phys. Commun. 220\, 341
  (2017). L. Y. Barash\, M. Weigel\, L. N Shchur\, and W. Janke\, Explori
 ng first-order phase transitions with population annealing\, Eur. Phys. 
 J. Special Topics 226\, 595 (2017).
LOCATION:Physics (PS0.17)
CATEGORIES:
LAST-MODIFIED:20181026T092719Z
ORGANIZER;CN=Peter Brommer:
END:VEVENT
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