Accelerating knowledge-based energy evaluation in protein structure modeling with Graphics Processing Units

Evaluating the energy of a protein molecule is one of the most computationally costly operations in many protein structure modeling applications. In this paper, we present an efficient implementation of knowledge-based energy functions by taking advantage of the recent Graphics Processing Unit (GPU) architectures. We use DFIRE, a knowledge-based all-atom potential, as an example to demonstrate our GPU implementations on the latest NVIDIA Fermi architecture. A load balancing workload distribution scheme is designed to assign computations of pair-wise atom interactions to threads to achieve perfect or near-perfect load balancing in the symmetric NN-body problem in DFIRE. Reorganizing atoms in the protein also improves the cache efficiency in Fermi GPU architecture, which is particularly effective for small proteins. Our DFIRE implementation on GPU (GPU-DFIRE) has exhibited a speedup of up to ∼∼150 on NVIDIA Quadro FX3800M and ∼∼250 on NVIDIA Tesla M2050 compared to the serial DFIRE implementation on CPU. Furthermore, we show that protein structure modeling applications, including a Monte Carlo sampling program and a local optimization program, can benefit from GPU-DFIRE with little programming modification but significant computational performance improvement.

► Using GPU to accelerate knowledge-based energy function evaluation in protein structure modeling.
► Workload distribution scheme to achieve perfect or nearly perfect load balancing in symmetric N-body problem.
► Reordering protein atom sequence to achieve GPU cache efficiency.
► Performance improvements in protein structure modeling applications.