Abstract
Large-scale simulations and computational modeling using molecular
dynamics (MD) continues to make significant impacts in the field of
biology. It is well known that simulations of biological events at native
time and length scales requires computing power several orders of
magnitude beyond today's commonly available systems. Supercomputers,
such as IBM Blue Gene/L and Cray XT3, will soon make tens to
hundreds of teraFLOP/s of computing power available by utilizing
thousands of processors. The popular algorithms and MD applications,
however, were not initially designed to run on thousands of
processors. In this paper, we present detailed investigations of the
performance issues, which are crucial for improving the scalability of
the MD-related algorithms and applications on massively parallel
processing (MPP) architectures. Due to the varying characteristics of
biological input problems, we study two prototypical biological
complexes that use the MD algorithm: an explicit solvent and an
implicit solvent. In particular, we study the AMBER application, which
supports a variety of these types of input problems. For the explicit
solvent problem, we focused on the particle mesh Ewald (PME) method
for calculating the electrostatic energy, and for the implicit solvent
model, we targeted the Generalized Born (GB) calculation. We uncovered
and subsequently modified a limitation in AMBER that restricted the
scaling beyond 128 processors. We collected performance
data for experiments on up to 2048 Blue Gene/L and XT3 processors and
subsequently identified that the
scaling is largely limited by the underlying algorithmic
characteristics and also by the implementation of the
algorithms. Furthermore, we found that the input problem size of
biological system is constrained by memory available per node. In
conclusion, our results indicate that MD codes can significantly
benefit from the current generation architectures with relatively
modest optimization efforts. Nevertheless, the key for enabling
scientific breakthroughs lies in exploiting the full potential of
these new architectures.