Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

Computational geometry

Image processing pipelines combine the challenges of stencil computations and stream programs. They are composed of large graphs of different stencil stages, as well as complex reductions, and stages with global or data-dependent access patterns. Because of their complex structure, the performance difference between a naive implementation of a pipeline and an optimized one is often an order of magnitude. Efficient implementations require optimization of both parallelism and locality, but due to the nature of stencils, there is a fundamental tension between parallelism, locality, and introducing redundant recomputation of shared values.We present a systematic model of the tradeoff space fundamental to stencil pipelines, a schedule representation which describes concrete points in this space for each stage in an image processing pipeline, and an optimizing compiler for the Halide image processing language that synthesizes high performance implementations from a Halide algorithm and a schedule. Combining this compiler with stochastic search over the space of schedules enables terse, composable programs to achieve state-of-the-art performance on a wide range of real image processing pipelines, and across different hardware architectures, including multicores with SIMD, and heterogeneous CPU+GPU execution. From simple Halide programs written in a few hours, we demonstrate performance up to 5x faster than hand-tuned C, intrinsics, and CUDA implementations optimized by experts over weeks or months, for image processing applications beyond the reach of past automatic compilers.

Halide: a language and compiler for optimizing parallelism, locality, and recomputation in image processing pipelines

Molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) and molecular mechanics generalized Born surface area (MM/GBSA) are arguably very popular methods for binding free energy prediction since they are more accurate than most scoring functions of molecular docking and less computationally demanding than alchemical free energy methods. MM/PBSA and MM/GBSA have been widely used in biomolecular studies such as protein folding, protein-ligand binding, protein-protein interaction, etc. In this review, methods to adjust the polar solvation energy and to improve the performance of MM/PBSA and MM/GBSA calculations are reviewed and discussed. The latest applications of MM/GBSA and MM/PBSA in drug design are also presented. This review intends to provide readers with guidance for practically applying MM/PBSA and MM/GBSA in drug design and related research fields.

End-Point Binding Free Energy Calculation with MM/PBSA and MM/GBSA: Strategies and Applications in Drug Design

Molecular docking methodology explores the behavior of small molecules in the binding site of a target protein. As more protein structures are determined experimentally using X-ray crystallography or nuclear magnetic resonance (NMR) spectroscopy, molecular docking is increasingly used as a tool in drug discovery. Docking against homology-modeled targets also becomes possible for proteins whose structures are not known. With the docking strategies, the druggability of the compounds and their specificity against a particular target can be calculated for further lead optimization processes. Molecular docking programs perform a search algorithm in which the conformation of the ligand is evaluated recursively until the convergence to the minimum energy is reached. Finally, an affinity scoring function, ΔG [U total in kcal/mol], is employed to rank the candidate poses as the sum of the electrostatic and van der Waals energies. The driving forces for these specific interactions in biological systems aim toward complementarities between the shape and electrostatics of the binding site surfaces and the ligand or substrate.

Software for molecular docking: a review

Current wireless networks mainly focus on delay-tolerant applications while demands for latency-sensitive applications are rising with VR/AR technologies and machine-to-machine IoT applications. In this paper we consider multi-channel, multi-radio scheduling at the MAC layer to optimize for the performance of prioritized, delay-sensitive demands. Our objective is to design an interference-free schedule that minimizes the maximum weighted refresh time among all edges, where the refresh time of an edge is the maximum number of time slots between two successive slots of that edge and the weights reflect given priorities. In the single-antenna unweighted case with k channels and n transceivers, the scheduling problem reduces to the classical edge coloring problem when $k \ge \lfloor n/2 \rfloor $ and to strong edge coloring when $k=1$, but it is neither edge coloring nor strong edge coloring for general k. Further, the priority requirement introduces extra challenges. In this paper we provide a randomized algorithm with an approximation factor of $\tilde{O}\left( \max \left\{ \sqrt{\varDelta _p }, \frac{\varDelta _p}{\sqrt{k}} \right\} \log m \right) $ in expectation, where $\varDelta _p$ denotes the maximum degree of the unweighted multi-graph, which is formed by duplicating each edge $e_i$ for $w_i$ times ($w_i$ is $e_i$’s integral priority value), and m is the number of required link communications ($f(n) \in \tilde{O}(h(n))$ means that $f(n) \in O\left( h(n) \log ^k(h(n)) \right) $ for some positive constant k. The results are generalized to the multi-antenna settings. We evaluate the performance of our methods in different settings using simulations).

Multi-channel Assignment and Link Scheduling for Prioritized Latency-Sensitive Applications

This paper introduces the kissing problem: given a rectangular room with n people in it, what is the most efficient way for each pair of people to kiss each other goodbye? The room is viewed as a set of pixels that form a subset of the integer grid. At most one person can stand on a pixel at once, and people move horizontally or vertically. In order to move into a pixel in time step t, the pixel must be empty in time step t1.

The paper gives one algorithm for kissing everyone goodbye. 

This paper gives optimal solutions for small cases, which were found using a heuristic state space search (IDA*).

/pdf/the-kissing-problem-how-to-end-a-gathering-when-everyone-ietztn6rht.pdf

The Kissing Problem: How to End a Gathering When Everyone Kisses Everyone Else Goodbye

The binary-forking model is a parallel computation model, formally defined by Blelloch et al. very recently, in which a thread can fork a concurrent child thread, recursively and asynchronously. The model incurs a cost of $\Theta(\log n)$ to spawn or synchronize $n$ tasks or threads. The binary-forking model realistically captures the performance of parallel algorithms implemented using modern multithreaded programming languages on multicore shared-memory machines. In contrast, the widely studied theoretical PRAM model does not consider the cost of spawning and synchronizing threads, and as a result, algorithms achieving optimal performance bounds in the PRAM model may not be optimal in the binary-forking model. Often, algorithms need to be redesigned to achieve optimal performance bounds in the binary-forking model and the non-constant synchronization cost makes the task challenging. 
Though the binary-forking model allows the use of atomic {\em test-and-set} (TS) instructions to reduce some synchronization overhead, assuming the availability of such instructions puts a stronger requirement on the hardware and may limit the portability of the algorithms using them. In this paper, we avoid the use of locks and atomic instructions in our algorithms except possibly inside the join operation which is implemented by the runtime system. 
In this paper, we design efficient parallel algorithms in the binary-forking model without atomics for three fundamental problems: Strassen's (and Strassen-like) matrix multiplication (MM), comparison-based sorting, and the Fast Fourier Transform (FFT). All our results improve over known results for the corresponding problem in the binary-forking model both with and without atomics.

Low-Depth Parallel Algorithms for the Binary-Forking Model without Atomics.

Optimizing Read Reversals for Sequence Compression - (Extended Abstract).

Computing the polarization energy between a ligand (i.e., a small molecule such as a drug molecule) and a receptor (e.g., a virus molecule) is of utmost importance in drug design. We have designed and implemented distributed-memory and distributed-shared-memory parallel algorithms for approximating GB-polarization energy (e.g., polar part of free energy of hydration) of protein molecules. This is an octree-based hierarchical algorithm, built on Greengard-Rokhlin type near-far decomposition of data points (i.e., atoms and points sampled from the molecular surface) for calculating the polarization energy of protein molecules using the surface based r6-approximation of Generalized Born radii of atoms. We have shown that our implementations outperform state-of-the-art GB-polarization energy implementations, such as Amber 12, GBr6, Gromacs 4.5.3, NAMD 2.9 and Tinker 6.0. Using approximations, cache-efficient data structures and efficient load-balancing schemes, we achieve a speedup factor of ~ 400 w.r.t Amber with less than 1% error w.r.t. the naive exact algorithm using as few as 144 cores (i.e., 12 compute nodes with 12 cores each) for molecules with as many as half a million atoms.

/pdf/polarization-energy-on-a-cluster-of-multicores-47nzsgqru0.pdf

Rezaul Chowdhury

Papers

Multi-channel Assignment and Link Scheduling for Prioritized Latency-Sensitive Applications

The Kissing Problem: How to End a Gathering When Everyone Kisses Everyone Else Goodbye

Low-Depth Parallel Algorithms for the Binary-Forking Model without Atomics.

Optimizing Read Reversals for Sequence Compression - (Extended Abstract).

Polarization Energy on a Cluster of Multicores