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Scaling the ISAM Land Surface Model through Parallelization of Inter-component Data Transfer
2014 43rd International Conference on Parallel Processing
We present the progression of developments necessary to scale the ISAM land surface model from single nodes and small clusters with unusually large per-node memory to much larger systems with more common configurations. These efforts include load balancing, conventional library-based output parallelization to reduce memory load, and parallel-in-time data input. On Hopper, a Cray XE6 machine, the result was strong scaling from 256 cores to 16k cores with an efficiency of 32.9%. On Edison, a Craydoi:10.1109/icpp.2014.51 dblp:conf/icpp/MillerREBZJK14 fatcat:bexogcjykzgnfa3od7nx2lxtry