The NCI approach is a modern tool to reveal chemical noncovalent interactions. It is particularly attractive to describe ligand-protein binding. A custom implementation for NCI using promolecular density is presented. It is designed to leverage the computational power of NVIDIA graphics processing unit (GPU) accelerators through the CUDA programming model. The code performances of three versions are examined on a test set of 144 systems. NCI calculations are particularly well suited to the GPU architecture, which reduces drastically the computational time. On a single compute node, the dual-GPU version leads to a 39-fold improvement for the biggest instance compared to the optimal OpenMP parallel run (C code, icc compiler) with 16 CPU cores. Energy consumption measurements carried out on both CPU and GPU NCI tests show that the GPU approach provides substantial energy savings. © 2017 Wiley Periodicals, Inc.
Abstract. In the simulation of complex multi-scale flows arising in weather and climate modelling, one of the biggest challenges is to satisfy strict service requirements in terms of time to solution and to satisfy budgetary constraints in terms of energy to solution, without compromising the accuracy and stability of the application. These simulations require algorithms that minimise the energy footprint along with the time required to produce a solution, maintain the physically required level of accuracy, are numerically stable, and are resilient in case of hardware failure. The European Centre for Medium-Range Weather Forecasts (ECMWF) led the ESCAPE (Energy-efficient Scalable Algorithms for Weather Prediction at Exascale) project, funded by Horizon 2020 (H2020) under the FET-HPC (Future and Emerging Technologies in High Performance Computing) initiative. The goal of ESCAPE was to develop a sustainable strategy to evolve weather and climate prediction models to next-generation computing technologies. The project partners incorporate the expertise of leading European regional forecasting consortia, university research, experienced high-performance computing centres, and hardware vendors. This paper presents an overview of the ESCAPE strategy: (i) identify domain-specific key algorithmic motifs in weather prediction and climate models (which we term Weather & Climate Dwarfs), (ii) categorise them in terms of computational and communication patterns while (iii) adapting them to different hardware architectures with alternative programming models, (iv) analyse the challenges in optimising, and (v) find alternative algorithms for the same scheme. The participating weather prediction models are the following: IFS (Integrated Forecasting System); ALARO, a combination of AROME (Application de la Recherche à l'Opérationnel à Meso-Echelle) and ALADIN (Aire Limitée Adaptation Dynamique Développement International); and COSMO–EULAG, a combination of COSMO (Consortium for Small-scale Modeling) and EULAG (Eulerian and semi-Lagrangian fluid solver). For many of the weather and climate dwarfs ESCAPE provides prototype implementations on different hardware architectures (mainly Intel Skylake CPUs, NVIDIA GPUs, Intel Xeon Phi, Optalysys optical processor) with different programming models. The spectral transform dwarf represents a detailed example of the co-design cycle of an ESCAPE dwarf. The dwarf concept has proven to be extremely useful for the rapid prototyping of alternative algorithms and their interaction with hardware; e.g. the use of a domain-specific language (DSL). Manual adaptations have led to substantial accelerations of key algorithms in numerical weather prediction (NWP) but are not a general recipe for the performance portability of complex NWP models. Existing DSLs are found to require further evolution but are promising tools for achieving the latter. Measurements of energy and time to solution suggest that a future focus needs to be on exploiting the simultaneous use of all available resources in hybrid CPU–GPU arrangements.
Over recent years clusters of workstations have begun to replace systems of mainframes. These workstations can be used to behave as a parallel machine, and then could supply supercomputing performance at modest cost. We report an experiment of such a network within PVM framework for an industrial image analysis application. The goal of the application is to build an automated process of textile color pattern analysis. We plan to build several tools for parallel image analysis, and among those tools, we present here first the parallelization of a quantization algorithm and a way to parallelize a color image pyramid using the Parallel Virtual Machine environment. One important point is the use of the distributed storage that represents a set of workstations. This distribution allows the complete parallelization of the Read/Write operations.
Aim: Drug discovery is a long process, often taking decades of research endeavors. It is still an active area of research in both academic and industrial sectors with efforts on reducing time and cost. Computational simulations like molecular docking enable fast exploration of large databases of compounds and extract the most promising molecule candidates for further in vitro and in vivo tests. Structure-based molecular docking is a complex process mixing both surface exploration and energy estimation to find the minimal free energy of binding corresponding to the best interaction location. Methods: Hereafter, heterogeneous graph score (HGScore), a new scoring function is proposed and is developed in the context of a protein-small compound-complex. Each complex is represented by a heterogeneous graph allowing to separate edges according to their class (inter- or intra-molecular). Then a heterogeneous graph convolutional network (HGCN) is used allowing the discrimination of the information according to the edge crossed. In the end, the model produces the affinity score of the complex. Results: HGScore has been tested on the comparative assessment of scoring functions (CASF) 2013 and 2016 benchmarks for scoring, ranking, and docking powers. It has achieved good performances by outperforming classical methods and being among the best artificial intelligence (AI) methods. Conclusions: Thus, HGScore brings a new way to represent protein-ligand interactions. Using a representation that involves classical graph neural networks (GNNs) and splitting the learning process regarding the edge type makes the proposed model to be the best adapted for future transfer learning on other (protein-DNA, protein-sugar, protein-protein, etc.) biological complexes.
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