TADSim: Discrete Event-Based Performance Prediction for Temperature- Accelerated Dynamics

SM Mniszewski and C Junghans and AF Voter and D Perez and SJ Eidenbenz, ACM TRANSACTIONS ON MODELING AND COMPUTER SIMULATION, 25, 15 (2015).

DOI: 10.1145/2699715

Next-generation high-performance computing will require more scalable and flexible performance prediction tools to evaluate software-hardware co-design choices relevant to scientific applications and hardware architectures. We present a new class of tools called application simulators-parameterized fast-running proxies of large-scale scientific applications using parallel discrete event simulation. Parameterized choices for the algorithmic method and hardware options provide a rich space for design exploration and allow us to quickly find well- performing software-hardware combinations. We demonstrate our approach with a TADSim simulator that models the temperature-accelerated dynamics (TAD) method, an algorithmically complex and parameter-rich member of the accelerated molecular dynamics (AMD) family of molecular dynamics methods. The essence of the TAD application is captured without the computational expense and resource usage of the full code. We accomplish this by identifying the time-intensive elements, quantifying algorithm steps in terms of those elements, abstracting them out, and replacing them by the passage of time. We use TADSim to quickly characterize the runtime performance and algorithmic behavior for the otherwise long- running simulation code. We extend TADSim to model algorithm extensions, such as speculative spawning of the compute-bound stages, and predict performance improvements without having to implement such a method. Validation against the actual TAD code shows close agreement for the evolution of an example physical system, a silver surface. Focused parameter scans have allowed us to study algorithm parameter choices over far more scenarios than would be possible with the actual simulation. This has led to interesting performance-related insights and suggested extensions.

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