Out-of-core macromolecular simulations on multithreaded architectures

Abstract

We address the solution of large-scale eigenvalue problems that appear in the motion simulation o f com- plex macromolecules on multithreaded platforms, consisting of multicore processors and possibly a graphics processor (graphics processing unit). In particular, we compare specialized implementations of several high- performance eigensolvers that, by relying on disk storage and out-of-core techniques, can in principle tackle the large memory requirements of these biological problems, which in general do not fit into the main memory of current desktop machines. All these out-of-core eigensolvers, except for one, are composed of compute-bound (i.e., arithmetically intensive) operations, which we accelerate by exploiting the performance of current multicore processors and, in some cases, by additionally off-loading certain parts of the computa- tion to a graphics processing unit accelerator. One of the eigensolvers is a memory-bound algorithm, which strongly constrains its performance when the data is on disk. However, this method exhibits a much lower arithmetic cost compared with its compute-bound alternatives for this particular application. Experimental results on a desktop platform, representative of current server technology, illustrate the potential of these methods to address the simulation of biological activity.