P20: Facilitating the Scalability of ParSplice for
Exascale Testbeds
SessionPoster Reception
Authors
Event Type
ACM Student Research Competition
Poster
Reception
TimeTuesday, November 14th5:15pm -
7pm
LocationFour Seasons Ballroom
DescriptionParallel trajectory splicing (ParSplice) is an attempt
to solve the enduring challenge of simulating the
evolution of materials over long time scales for complex
atomistic systems. A novel version of ParSplice is
introduced with features that could be useful in its
scaling to exascale architectures. A two-pronged
approach is used. First, latent parallelism is exploited
by extending support to heterogeneous architectures,
including GPUs and KNLs. Second, the efficiency of the
Kinetic Monte Carlo predictor is improved, allowing
enhanced parallel speculative execution. The key idea in
these predictor modifications is to include statistics
from higher temperature simulations. The issue of
inherent uncertainty in the prediction model was
addressed in order to improve the performance, as the
current predictor only takes into account the previous
observations to formulate the problem. The predictor was
also improved by using a hybrid approach of
message-passing + multi-threading.
(LA-UR-17-26181)
Authors
