A GPU-accelerated Direct-sum Boundary Integral Poisson-Boltzmann Solver

In this paper, we present a GPU-accelerated direct-sum boundary integral method to solve the linear Poisson-Boltzmann (PB) equation. In our method, a well-posed boundary integral formulation is used to ensure the fast convergence of Krylov subspace based linear algebraic solver such as the GMRES. The molecular surfaces are discretized with flat triangles and centroid collocation. To speed up our method, we take advantage of the parallel nature of the boundary integral formulation and parallelize the schemes within CUDA shared memory architecture on GPU. The schemes use only $11N+6N_c$ size-of-double device memory for a biomolecule with $N$ triangular surface elements and $N_c$ partial charges. Numerical tests of these schemes show well-maintained accuracy and fast convergence. The GPU implementation using one GPU card (Nvidia Tesla M2070) achieves 120-150X speed-up to the implementation using one CPU (Intel L5640 2.27GHz). With our approach, solving PB equations on well-discretized molecular surfaces with up to 300,000 boundary elements will take less than about 10 minutes, hence our approach is particularly suitable for fast electrostatics computations on small to medium biomolecules

Enabling and scaling biomolecular simulations of 100 million atoms on petascale machines with a multicore-optimized message-driven runtime

A 100-million-atom biomolecular simulation with NAMD is one of the three benchmarks for the NSF-funded sustainable petascale machine. Simulating this large molecular system on a petascale machine presents great challenges, including handling I/O, large memory footprint and getting good strong-scaling results. In this paper, we present parallel I/O techniques to enable the simula-tion. A new SMP model is designed to efficiently utilize ubiquitous wide multicore clusters by extending the CHARM++ asynchronous message-driven runtime. We exploit node-aware techniques to op-timize both the application and the underlying SMP runtime. Hi-erarchical load balancing is further exploited to scale NAMD to the full Jaguar PF Cray XT5 (224,076 cores) at Oak Ridge Na-tional Laboratory, both with and without PME full electrostatics, achieving 93 % parallel efficiency (vs 6720 cores) at 9 ms per step for a simple cutoff calculation. Excellent scaling is also obtained on 65,536 cores of the Intrepid Blue Gene/P at Argonne National Laboratory. 1

Path Similarity Analysis: a Method for Quantifying Macromolecular Pathways

Diverse classes of proteins function through large-scale conformational changes; sophisticated enhanced sampling methods have been proposed to generate these macromolecular transition paths. As such paths are curves in a high-dimensional space, they have been difficult to compare quantitatively, a prerequisite to, for instance, assess the quality of different sampling algorithms. The Path Similarity Analysis (PSA) approach alleviates these difficulties by utilizing the full information in 3N-dimensional trajectories in configuration space. PSA employs the Hausdorff or Fr\'echet path metrics---adopted from computational geometry---enabling us to quantify path (dis)similarity, while the new concept of a Hausdorff-pair map permits the extraction of atomic-scale determinants responsible for path differences. Combined with clustering techniques, PSA facilitates the comparison of many paths, including collections of transition ensembles. We use the closed-to-open transition of the enzyme adenylate kinase (AdK)---a commonly used testbed for the assessment enhanced sampling algorithms---to examine multiple microsecond equilibrium molecular dynamics (MD) transitions of AdK in its substrate-free form alongside transition ensembles from the MD-based dynamic importance sampling (DIMS-MD) and targeted MD (TMD) methods, and a geometrical targeting algorithm (FRODA). A Hausdorff pairs analysis of these ensembles revealed, for instance, that differences in DIMS-MD and FRODA paths were mediated by a set of conserved salt bridges whose charge-charge interactions are fully modeled in DIMS-MD but not in FRODA. We also demonstrate how existing trajectory analysis methods relying on pre-defined collective variables, such as native contacts or geometric quantities, can be used synergistically with PSA, as well as the application of PSA to more complex systems such as membrane transporter proteins.Comment: 9 figures, 3 tables in the main manuscript; supplementary information includes 7 texts (S1 Text - S7 Text) and 11 figures (S1 Fig - S11 Fig) (also available from journal site

A hybrid framework of iterative MapReduce and MPI for molecular dynamics applications

Developing platforms for large scale data processing has been a great interest to scientists. Hadoop is a widely used computational platform which is a fault-tolerant distributed system for data storage due to HDFS (Hadoop Distributed File System) and performs fault-tolerant distributed data processing in parallel due to MapReduce framework. It is quite often that actual computations require multiple MapReduce cycles, which needs chained MapReduce jobs. However, Design by Hadoop is poor in addressing problems with iterative structures. In many iterative problems, some invariant data is required by every MapReduce cycle. The same data is uploaded to Hadoop file system in every MapReduce cycle, causing repeated data delivering and unnecessary time cost in transferring this data. In addition, although Hadoop can process data in parallel, it does not support MPI in computing. In any Map/Reduce task, the computation must be serial. This results in inefficient scientific computations wrapped in Map/Reduce tasks because the computation can not be distributed over a Hadoop cluster, especially a Hadoop cluster on a traditional high performance computing cluster. Computational technologies have been extensively investigated to be applied into many application domains. Since the presence of Hadoop, scientists have applied the MapReduce framework to biological sciences, chemistry, medical sciences, and other areas to efficiently process huge data sets. In our research, we proposed a hybrid framework of iterative MapReduce and MPI for molecular dynamics applications. We carried out molecular dynamics simulations with the implemented hybrid framework. We improved the capability and performance of Hadoop by adding a MPI module to Hadoop. The MPI module enables Hadoop to monitor and manage the resources of Hadoop cluster so that computations incurred in Map/Reduce tasks can be performed in a parallel manner. We also applied the local caching mechanism to avoid data delivery redundancy to make the computing more efficient. Our hybrid framework inherits features of Hadoop and improves computing efficiency of Hadoop. The targeting application domain of our research is molecular dynamics simulation. However, the potential use of our iterative MapReduce framework with MPI is broad. It can be used by any applications which contain single or multiple MapReduce iterations, invoke serial or parallel (MPI) computations in Map phase or Reduce phase of Hadoop

Parallel simulation of reinforced concrete sructures using peridynamics

The failure of concrete structures involves many complex mechanisms. Traditional theoretical models are limited to specific problems and are not applicable to many real-life problems. Consequently, design specifications mostly rely on empirical equations derived from laboratory tests at the component level. It is desirable to develop new analysis methods, capable of harnessing material-level test parameters. To overcome limitations and shortcomings of models based on continuum mechanics and fracture mechanics, Stewart Silling introduced the concept of peridynamics in 1998. Similar to molecular dynamics, peridynamic modeling of a physical structure involves simulating interacting particles subjected to an empirical force field. The evolution of interacting particles determines the deformation of the structure at a given time due to the applied boundary condition. As a particle-based model, peridynamics requires the repeated evaluation of many particle interactions which is computationally demanding. However, with todays inexpensive computing hardware, parallel algorithms can be utilized to run such problems on multi-node supercomputers with fast interconnects. However, existing codes tend to be domain-specific with too many built-in physical assumptions. In this work, a novel method for parallelization of any particle-based simulation is presented which is quite general and suitable for simulating diverse physical structures. A scalable parallel code for molecular dynamics and peridynamics simulation, PDQ, is described which implements a novel wall method parallelization algorithm, developed as part of this thesis. PDQ partitions the geometric domain of a problem across multi-nodes while the physics is left open to the user to decide whether to simulate a solvated protein or alloy grain boundary at the atomic scale or to simulate cracking phenomena in concrete via peridynamics. A further extension of PDQ brings more flexibility by allowing the user to define any desired number of degrees of freedom for each particle in a peridynamics simulation. At the end of this thesis, plain, reinforced and prestressed concrete benchmark problems are simulated using PDQ and the results are compared to available design code equations or analytical solutions. This research is a step toward next level of computational modeling of reinforced concrete structures and the revolutionizing of how concrete is analyzed and also how concrete structures are designed.\u2