Performance characteristics of biomolecular simulations on high-end systems with multi-core processors

Biological processes occurring inside cell involve multiple scales of time and length; many popular theoretical and computational multi-scale techniques utilize biomolecular simulations based on molecular dynamics. Till recently, the computing power required for simulating the relevant scales was even beyond the reach of fastest supercomputers. The availability of petaFLOPS-scale computing power in near future holds great promise. Unfortunately, the biosimulations software technology has not kept up with the changes in hardware. In particular, with the introduction of multi-core processing technologies in systems with tens of thousands of processing cores, it is unclear whether the existing biomolecular simulation frameworks will be able to scale and to utilize these resources effectively. While the multi-core processing systems provide higher processing capabilities, their memory and network subsystems are posing new challenges to application and system software developers. In this study, we attempt to characterize computation, communication and memory efficiencies of biomolecular simulations on Teraflops-scale Cray XT systems, which contain dual-core Opteron processors. We identify that the application efficiencies using the multi-core processors reduce with the increase of the simulated system size. Further, we measure the communication overhead of using both cores in the processor simultaneously and identify that the slowdown in the MPI communication performance can significantly lower the achievable performance in the dual-core execution mode. We conclude that not only the biomolecular simulations need to be aware of the underlying multi-core hardware in order to achieve maximum performance but also the system software needs to provide processor and memory placement features in the high-end systems. Our results on stand-alone multi-core AMD and Intel systems confirm that combinations of processor and memory affinity schemes cause significant performance variations for our target test cases.

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