Computer simulation using particles
Computer simulation using particles
General purpose molecular dynamics simulations fully implemented on graphics processing units
Journal of Computational Physics
Resource-efficient computing paradigm for computational protein modeling applications
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
Scaling Hierarchical N-body Simulations on GPU Clusters
Proceedings of the 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis
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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 N-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.