Editing Monte Carlo method (section)

== Computational costs ==
Despite its conceptual and algorithmic simplicity, the computational cost associated with a Monte Carlo simulation can be staggeringly high. In general the method requires many samples to get a good approximation, which may incur an arbitrarily large total runtime if the processing time of a single sample is high.<ref name="sw09">{{cite book|author-last1=Shonkwiler |author-first1=R. W. |author-last2=Mendivil |author-first2=F. |title=Explorations in Monte Carlo Methods |year=2009 |publisher=Springer}}</ref> Although this is a severe limitation in very complex problems, the [[embarrassingly parallel]] nature of the algorithm allows this large cost to be reduced (perhaps to a feasible level) through [[parallel computing]] strategies in local processors, clusters, cloud computing, GPU, FPGA, etc.<ref>{{cite journal|author-last1=Atanassova |author-first1=E. |author-last2=Gurov |author-first2=T. |author-last3=Karaivanova |author-first3=A. |author-last4=Ivanovska |author-first4=S. |author-last5=Durchova |author-first5=M. |author-last6=Dimitrov |author-first6=D. |year=2016 |title=On the parallelization approaches for Intel MIC architecture |journal=AIP Conference Proceedings |volume=1773 |issue=1 |pages=070001 |doi=10.1063/1.4964983 |bibcode=2016AIPC.1773g0001A}}</ref><ref>{{cite journal|author-last1=Cunha Jr |author-first1=A. |author-last2=Nasser |author-first2=R. |author-last3=Sampaio |author-first3=R. |author-last4=Lopes |author-first4=H. |author-last5=Breitman |author-first5=K. |year=2014 |title=Uncertainty quantification through the Monte Carlo method in a cloud computing setting |journal=Computer Physics Communications |volume=185 |issue=5 |pages=1355–1363 |doi=10.1016/j.cpc.2014.01.006 |arxiv=2105.09512 |bibcode=2014CoPhC.185.1355C |s2cid=32376269}}</ref><ref>{{cite journal|author-last1=Wei |author-first1=J. |author-last2=Kruis |author-first2=F.E. |year=2013 |title=A GPU-based parallelized Monte-Carlo method for particle coagulation using an acceptance–rejection strategy |journal=Chemical Engineering Science |volume=104 |pages=451–459 |doi=10.1016/j.ces.2013.08.008|bibcode=2013ChEnS.104..451W }}</ref><ref>{{cite journal|author-last1=Lin |author-first1=Y. |author-last2=Wang |author-first2=F. |author-last3=Liu |author-first3=B. |year=2018 |title=Random number generators for large-scale parallel Monte Carlo simulations on FPGA |journal = Journal of Computational Physics |volume=360 |pages=93–103 |doi=10.1016/j.jcp.2018.01.029 |bibcode=2018JCoPh.360...93L}}</ref>