high performance computing on graphics processing units: hgpu.org

Many computational protein modeling applications using numerical methods such as Molecular Dynamics (MD), Monte Carlo (MC), or Genetic Algorithms (GA) require a large number of energy estimations of the protein molecular system. A typical energy function describing the protein energy is a combination of a number of terms characterizing various interactions within the protein molecule as well as the protein-solvent interactions. Evaluating the energy function of a relatively large protein molecule is rather computationally costly and usually occupies the major computation time in the protein simulation process. In this paper, we present a resource-efficient computing paradigm based on “consolidation” to reduce the computational time of evaluating the energy function of large protein molecule. The fundamental idea of consolidation is to increase computational density to a computer in order to increase the CPU utilizations. Consolidation will be particularly efficient when the consolidated computations have heterogeneous resource demands. In computational protein modeling applications with costly energy function evaluation, we advocate the use of “thread consolidation,” which is to spawn concurrent threads to carry out parallel energy function terms computations. Our computational results show that 7%-11% speedup in a protein loop structure prediction program on various hardware architectures where memory-intensive and computation-intensive terms coexist in the energy function. For an MD protein simulation program where computation-intensive energy function evaluations are divided and carried out by concurrent threads, we also find slight performance improvement when the thread consolidation technique is applied.