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==Minimization==
One of the challenges for exascale algorithm design is to minimize or reduce synchronization.
Synchronization takes more time than computation, especially in distributed computing. Reducing synchronization drew attention from computer scientists for decades. Whereas it becomes an increasingly significant problem recently as the gap between the improvement of computing and latency increases. Experiments have shown that (global) communications due to synchronization on distributed computers takes a dominated share in a sparse iterative solver.<ref>{{cite journal|title=Minimizing synchronizations in sparse iterative solvers for distributed supercomputers |author=Shengxin, Zhu and Tongxiang Gu and Xingping Liu|journal=Computers & Mathematics with Applications|volume=67|issue=1|pages=199–209|year=2014|doi=10.1016/j.camwa.2013.11.008|doi-access=free|hdl=10754/668399|hdl-access=free}}</ref> This problem is receiving increasing attention after the emergence of a new benchmark metric, the High Performance Conjugate Gradient (HPCG),<ref>{{cite web|url=http://hpcg-benchmark.org/|title=HPCG Benchmark}}</ref> for ranking the top 500 supercomputers.
==Problems==
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