Paper
:
A Massively Parallel Infrastructure for Adaptive Multiscale Simulations: Modeling RAS Initiation Pathway for Cancer
Event Type
Paper
Registration Categories
TP
Tags
Applications
Cancer
Compiler Analysis and Optimization
Compilers
Computational Biology
Exascale
Fault Tolerance
Heterogeneous Systems
Machine Learning
Parallel Application Frameworks
Portability
Reliability
Resiliency
Runtime Systems
Scalable Computing
Scientific Computing
Scientific Workflows
Simulation
Tools
Workflows
Award Finalists
BP Finalist
TimeWednesday, 20 November 20193:30pm - 4pm
DescriptionMost biological phenomena have microscopic foundations yet span macroscopic length- and time-scales, necessitating multiscale computational models. Efficient simulation of these complex multiscale models on modern heterogeneous architectures poses significant challenges in scheduling and co-managing resources such as computational power, communication bottlenecks, and filesystem bandwidth. To address these challenges, we present a novel massively parallel Multiscale Machine-Learned Modeling Infrastructure (MuMMI), which combines a large length- and time-scale macro model with a high-fidelity molecular dynamics (MD) micro model using machine learning. We describe our infrastructure which is designed for high scalability, efficiency, robustness, portability, and fault tolerance on heterogeneous resources. We demonstrate MuMMI conducting the largest-of-its-kind simulation to investigate the dynamics of KRAS proteins in cancer initiation. Concurrently running up to 36,000 jobs on 16,000 GPUs and 176,000 CPU cores, we executed 120,000 MD simulations surpassing an aggregate simulation time of 200 milliseconds, orders of magnitude greater than comparable studies.
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