Patrick Lavin
Richard Vuduc
ABSTRACT
As computer systems grow larger and more complex, it takes more time to simulate their behavior in detail. Researchers interested in simulating large-scale systems must choose between less-accurate high-level models or simulating smaller portions of their benchmark suite, both of which are highly manual, offline approaches that require time-consuming analysis by experts. Multifidelity simulation aims to lessen this burden by automatically adapting the fidelity of a simulation to the complexity of the behavior occurring at any given point in time. We show how a multifidelity memory system model can be used to accelerate single node simulation by up to 2x with 1-5% mean absolute percent error in the simulated instructions per cycle across benchmark suites.
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Index Terms
- Multifidelity Memory System Simulation in SST
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